Journal articles on the topic 'Multi-failures mode'
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Nappa, Dario, and Gergana I. Drandova. "Estimating activation energies for multi-mode failures." Microelectronics Reliability 54, no. 2 (February 2014): 349–53. http://dx.doi.org/10.1016/j.microrel.2013.09.025.
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Hou, Zhi. "Decision Method of Equipment Reliability Assurance Based on Multi-Level Management." Applied Mechanics and Materials 101-102 (September 2011): 832–37. http://dx.doi.org/10.4028/www.scientific.net/amm.101-102.832.
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This paper classifies reliability assurance management strategies into four modes, i.e., reactive mode, preventive mode, predictive mode and proactive mode, advanced a decision method of equipment reliability assurance based on multilevel management. The risks of equipment failures are also categorized into four levels according to their degrees of severity and occurring rates. In order to improve pertinence and reduce cost of reliability assurance, it is proposed to adopt a multi-level management strategy in handling the risks. For this purpose, a model for relationship between risk levels and reliability assurance modes, a model for equipment reliability assurance cost and a model for equipment risk increments after reliability assurance have been established. The applied risk management mode for each concrete risk is determined by minimizing the total reliability assurance cost with risk increment serving as a constraint condition. The proposed approach has been demonstrated by using an example.
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Haktanır, Elif, and Cengiz Kahraman. "Interval-valued neutrosophic failure mode and effect analysis." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6591–601. http://dx.doi.org/10.3233/jifs-189121.
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Failure mode and effects analysis (FMEA) is a structured approach for discovering possible failures that may occur in the design of a product or process. Since classical FMEA is not sufficient to represent the vagueness and impreciseness in human decisions and evaluations, many extensions of ordinary fuzzy sets such as hesitant fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, spherical fuzzy sets, and picture fuzzy sets. Classical FMEA has been handled to capture the uncertainty through these extensions. Neutrosophic sets is a different extension from the others handling the uncertainty parameters independently. A novel interval-valued neutrosophic FMEA method is developed in this study. The proposed method is presented in several steps with its application to an automotive company in order to prioritize the potential causes of failures during the design process by considering multi-experts’ evaluations.
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Liu, Xiangxiang, Lingling Li, Diganta Das, Ijaz Haider Naqvi, and Michael G. Pecht. "Online Degradation State Assessment Methodology for Multi-Mode Failures of Insulated Gate Bipolar Transistor." IEEE Access 8 (2020): 69471–81. http://dx.doi.org/10.1109/access.2020.2984385.
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Zhang, Xi, Hongju Wang, Xuehui Li, Shoujun Gao, Kui Guo, and Yingle Wei. "Fault Diagnosis of Mine Ventilator Bearing Based on Improved Variational Mode Decomposition and Density Peak Clustering." Machines 11, no. 1 (December 26, 2022): 27. http://dx.doi.org/10.3390/machines11010027.
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The mine ventilator plays a role in protecting the life safety of underground workers, which is very significant to the production and development of coal mines. In total, 70% of ventilator failures are mechanical failures, and bearing failures are the most likely to occur in mechanical failures, which are also difficult to find. In order to identify fan bearing faults accurately, this paper proposes a fault diagnosis method based on improved variational mode decomposition and density peak clustering. First, the variational mode decomposition’s modal number K and secondary penalty factor α are chosen employing the improved sparrow optimization process. The bearing vibration signal is decomposed by the variational mode decomposition algorithm with optimized parameters. To create the characteristic vector, the multi-scale permutation entropy of the fourth order intrinsic mode function is determined. Then, the characteristic matrix is dimensionally reduced by kernel principal component analysis, and the two-dimensional matrix after dimensionality reduction is divided by density peak clustering method to find the clustering center of the training sample features. Lastly, the membership degree is assessed using the normalized clustering distance between the characteristic matrix of the test sample and the cluster center of the training sample. The accuracy of bearing fault identification on the self-constructed experimental platform can reach 100%, which verifies the effectiveness and potential of the proposed method.
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Hu, Yingjun, Anmin Zhang, Wuliu Tian, Jinfen Zhang, and Zebei Hou. "Multi-Ship Collision Avoidance Decision-Making Based on Collision Risk Index." Journal of Marine Science and Engineering 8, no. 9 (August 20, 2020): 640. http://dx.doi.org/10.3390/jmse8090640.
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Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.
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Dong, Lijing, and Kaige Liu. "Adaptive sliding mode control for uncertain nonlinear multi-agent tracking systems subject to node failures." Journal of the Franklin Institute 359, no. 2 (January 2022): 1385–402. http://dx.doi.org/10.1016/j.jfranklin.2021.11.039.
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Zhang, F., Z. Zhou, Y. Wang, D. Wang, M. Wu, and L. Zhu. "An SEU fault injection platform for radiation-harden design debugging in the FPGA." Journal of Instrumentation 17, no. 08 (August 1, 2022): P08007. http://dx.doi.org/10.1088/1748-0221/17/08/p08007.
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Abstract An SEU fault injection platform was designed to ease the debugging of radiation-harden design in FPGA. The platform includes the FPGA being tested, the functional firmware being debugged, the essential-bit extraction algorithm based on Python, and the random fault injection algorithm. The platform can inject single-bit SEU failures, adjacent two-bit SEU failures, and three-bit SEU failures. It works in two modes: normal injection mode and essential-bit injection mode. Functional failure rate is the performance metric which used to evaluate the development. It is the probability of triggering a DUT function failure. In this experiment, the SEU fault injection platform is verified by measuring whether TCP/IP communication links are disconnected due to SEU faults. Experimental results show that the probability of TCP/IP link disconnection in normal injection mode is 0.13%, 0.23% and 0.25% respectively when random injection of single-bit SEU failure, adjacent two-bit SEU failure and multi-bit SEU failure occurs 10,000 times. In the essential-bit injection mode, the probability of TCP/IP link disconnection caused by single-bit SEUs failure, adjacent two-bit SEUs failure and three-bit SEUs failure is 0.87%, 6.97% and 11.76% respectively, indicating a significant increase in the functional failure rates. This shows that the essential-bit injection mode can be used to expose problems more quickly and accelerate the debugging process of FPGA radiation-harden design.
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Powell, Emilie S., Lanty M. O’Connor, Anna P. Nannicelli, Lisa T. Barker, Rahul K. Khare, Nicholas P. Seivert, Jane L. Holl, and John A. Vozenilek. "Failure mode effects and criticality analysis: innovative risk assessment to identify critical areas for improvement in emergency department sepsis resuscitation." Diagnosis 1, no. 2 (June 1, 2014): 173–81. http://dx.doi.org/10.1515/dx-2014-0007.
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AbstractSepsis is an increasing problem in the practice of emergency medicine as the prevalence is increasing and optimal care to reduce mortality requires significant resources and time. Evidence-based septic shock resuscitation strategies exist, and rely on appropriate recognition and diagnosis, but variation in adherence to the recommendations and therefore outcomes remains. Our objective was to perform a multi-institutional prospective risk-assessment, using failure mode effects and criticality analysis (FMECA), to identify high-risk failures in ED sepsis resuscitation.We conducted a FMECA, which prospectively identifies critical areas for improvement in systems and processes of care, across three diverse hospitals. A multidisciplinary group of participants described the process of emergency department (ED) sepsis resuscitation to then create a comprehensive map and table listing all process steps and identified process failures. High-risk failures in sepsis resuscitation from each of the institutions were compiled to identify common high-risk failures.Common high-risk failures included limited availability of equipment to place the central venous catheter and conduct invasive monitoring, and cognitive overload leading to errors in decision-making. Additionally, we identified great variability in care processes across institutions.Several common high-risk failures in sepsis care exist: a disparity in resources available across hospitals, a lack of adherence to the invasive components of care, and cognitive barriers that affect expert clinicians’ decision-making capabilities. Future work may concentrate on dissemination of non-invasive alternatives and overcoming cognitive barriers in diagnosis and knowledge translation.
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Rachieru, Nicoleta, Nadia Belu, and Daniel Constantin Anghel. "Evaluating the Risk of Failure on Injection Pump Using Fuzzy FMEA Method." Applied Mechanics and Materials 657 (October 2014): 976–80. http://dx.doi.org/10.4028/www.scientific.net/amm.657.976.
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This research is aimed at utilizing failure mode and effect analysis (FMEA) which is a reliability analysis method applicable to rotary injection pump design. In traditional FMEA, Risk Priority Number (RPN) ranking system is used to evaluate, the risk level of failures to rank failures and to prioritize actions. RPN is obtained by multiplying the scores of three risk factors like the Severity (S), Occurrence (O) and Detection (D) of each failure mode. RPN method can not emphasise the nature of the problem, which is multi-attributable and has a group of experts' opinions. Furthermore, attributes are subjective and have different importance levels. In this paper, a framework is proposed to overcome the shortcomings of the traditional method through the fuzzy set theory. Two case studies have been shown to demonstrate the methodology thus developed. It is illustrated a parallel between the results obtained by the traditional method and fuzzy logic for determining the RPNs. We expect that fuzzy FMEA model will assist FMEA team in assess and rank risks more precisely compared with risk assessment model of method.
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Manfroi, Fernanda Borguetti, Maurem Leitão Marcondes, Deise Caren Somacal, Gilberto Antonio Borges, Luiz Henrique Burnett Júnior, and Ana Maria Spohr. "Bond Strength of a Novel One Bottle Multi-mode Adhesive to Human Dentin After Six Months of Storage." Open Dentistry Journal 10, no. 1 (June 6, 2016): 268–77. http://dx.doi.org/10.2174/1874210601610010268.
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Objective: The aim of the study was to evaluate the microtensile bond strength (µTBS) of Scotchbond Universal to dentin using the etch-and-rinse or the self-etch technique after 24 h and 6 months of storage. Materials and Methods: Flat dentin surfaces were obtained in 24 third molars. The teeth were divided into four groups: G1 – Scotchbond Universal applied in the etch-and-rinse mode; G2 – Scotchbond Universal applied in the self-etch mode; G3 – Scotchbond Multi-Purpose; G4 – Clearfil SE Bond. A block of composite was built on the adhesive area. The tooth/resin sets were cut parallel to the long axis to obtain 40 beams (~0.8 mm2) for each group. Twenty specimens were immediately submitted to the µTBS test, and the remaining 20 were stored in water for 6 months. Failures and the adhesive interface were analyzed by SEM. Results: According to two-way ANOVA, the interaction between adhesive and storage time was significant (p=0.015).The µTBS (MPa) means were the following: 24 h – G1 (39.37±10.82), G2 (31.02±13.76), G3 (35.09±14.03) and G4 (35.84±11.06); 6 months – G1 (36.99±8.78), G2 (40.58±8.07), G3 (32.44±6.07) and G4 (41.75±8.25). Most failures were mixed. Evidence of hybrid layer and numerous resin tags were noted for Scotchbond Universal applied with the etch-and-rinse mode and Scotchbond Multi-Purpose. A thinner hybrid layer and fewer resin tags were noted for Scotchbond Universal applied in the self-etch mode and Clearfil SE Bond. Conclusion: The results indicate that the µTBS for Scotchbond Universal is comparable to the gold-standard adhesives. Scotchbond Universal applied in the self-etch mode and Clearfil SE Bond revealed higher bond stability compared to the etch-and-rinse mode.
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M. Kizilcay and C. Neumann. "Mitigation of common mode failures at multi-circuit line configurations by application of line arresters against back-flashovers." Journal of Energy - Energija 59, no. 1-4 (August 22, 2022): 52–60. http://dx.doi.org/10.37798/2010591-4278.
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Due to the limited number of corridors multi circuit line configurations are often applied. These overhead lines frequently consist of high towers that are subject to lightning strokes. In case of higher current amplitudes and higher footing resistances due to bad earthing conditions back-flashovers are caused leading to common mode failures and to severe outages. The paper describes investigations performed by means of computer simulations to identify the towers of a multi-circuit line consisting of voltage levels 380 kV, 220 kV and 110 kV that are endangered by back-flashovers of the 110-kV double-circuit lines. The footing resistance of towers of the targeted line section has been measured by an instrument at high-frequency. Influence of various factors on the back-flashover over 110 kV insulator strings has been studied by means of EMTP-ATP simulations. Different current waveforms of the lightning stroke have been used to represent the first stroke and subsequent strokes. The towers are represented by the models described in [3], [8]. Available flashover analysis methods [7], [8], [12], [13] like leader development method by Pigini et al and by Motoyama, and voltage-time integration method by Kind have been applied. The towers at which back-flashover is more likely to occur than at other towers are identified by the time integral of voltage according to Kind. Various factors like tower footing impedance, tower surge impedance and tower height are considered. Application of line a surge arrester is shown to be a successful mitigation technique to reduce the back-flashover rate of those 110 kV lines. The lightning overvoltage performance of surge arresters has been analyzed by means of digital simulations. Based on the results of investigations line arresters were installed on the towers in question. Since the installation no further common mode failure has been observed.
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Ulfah, M., D. L. Trenggonowati, R. Ekawati, and S. Ramadhania. "The proposed improvements to minimize potential failures using lean six sigma and multi attribute failure mode analysis approaches." IOP Conference Series: Materials Science and Engineering 673 (December 10, 2019): 012082. http://dx.doi.org/10.1088/1757-899x/673/1/012082.
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Nima, Gabriel, Paulo Vitor Campos Ferreira, Andreia Bolzan de Paula, Simonides Consani, and Marcelo Giannini. "Effect of Metal Primers on Bond Strength of a Composite Resin to Nickel-Chrome Metal Alloy." Brazilian Dental Journal 28, no. 2 (April 2017): 210–15. http://dx.doi.org/10.1590/0103-6440201701288.
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Abstract This study evaluated the effects of three metal primers and one multi-mode adhesive system on the shear bond strength (SBS) of a flowable composite resin to nickel-chrome metal alloy (Ni-Cr). Ninety plates were cast from Ni-Cr and divided in nine groups (n=10). The surfaces were sandblasted with Al2O3 and primed with three adhesive primers: Alloy Primer (AP), Universal Primer (TP) and RelyX Ceramic Primer (CP), and a multi-mode adhesive (Scotchbond Universal, SU). The Adper Single Bond Plus (SB) and SU adhesives were also combined with adhesive primers. Control group did not have any surface treatment. The groups were: AP, AP+SB, AP+SU, TP+SB, TP+SU, CP+SB, CP+SU and SU. Composite cylinders were built on alloy surface. After 24 h, half the specimens were subjected to SBS and the other half to thermal cycling before testing. Data were analyzed by two-way ANOVA and Tukey’s test (a=0.05). Failure modes were assessed by SEM observation. Higher SBS were obtained with AP and TP combined with adhesives at 24 h and the lowest one for control group. Thermocycling reduced SBS for AP, CP+SU and SU. Combination between TP and SU resulted in the highest SBS after the thermocycling. TP groups showed all types of failures and high incidence of mixed failures. The use of AP and UP metal primers before application of SU and SB adhesive systems increased the SBS of composite to Ni-Cr. These combinations between metal primers and adhesives had the highest SBS after thermocycling.
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Iskandar, Ismed, and Noraini Mohd Razali. "Multi-Mode Failure Models for Attribute Test Data in Reliability Systems, a Bayesian Analysis Approach Using Multi-Nomial Distribution Model." Advanced Materials Research 903 (February 2014): 419–24. http://dx.doi.org/10.4028/www.scientific.net/amr.903.419.
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This paper describes the extending model of Multi-mode Failure Models by using the Weibull and Gamma distribution models presented in a conference [1,2]. Different than the models in the previous papers which are for variable test data, in this paper we will describe the use of attribute test data for our model. In reliability theory, the most important problem is to determine the reliability of a complex system from the reliability of its components. The weakness of most reliability theories is that the systems are described and explained as simply functioning or failed. In many real situations, the failures may be from many causes depending upon the age and the environment of the system and its components. Another problem in reliability theory is one of estimating the parameters of the assumed failure models. The estimation may be based on data collected over censored or uncensored life tests. In many reliability problems, the failure data are simply quantitatively inadequate. The Bayesian analyses are more beneficial than classical analyses in such cases. The Bayesian estimation analyses allow us to combine past knowledge or experience in the form of an apriori distribution with life test data to make inferences of the parameter of interest. In this paper, we have investigated the application of the Bayesian estimation analyses to multi-mode failure systems for attribute test data. The cases are limited to the models with independent causes of failure. We select our investigation by using the Multi-nomial distribution as our model. This distribution is widely used in reliability analysis for attribute test data. This model describes the likelihood function and follows with the description of the posterior function. A Beta prior is used in our analysis for each model and it is followed by the estimation of the point, interval, and reliability estimations.
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Derradji, Rima, and Rachida Hamzi. "Multi-criterion analysis based on integrated process-risk optimization." Journal of Engineering, Design and Technology 18, no. 5 (January 10, 2020): 1015–35. http://dx.doi.org/10.1108/jedt-08-2019-0201.
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Purpose This paper aims to propose a process optimization approach showing how organizations are able to achieve sustainable and efficient process optimization, based on integrated process-risk analysis using several criteria to a better decision-making. Design/methodology/approach Several approaches are used (functional/dysfunctional) to analyze how processes work and how to deal with risks forming multi-criteria decision-making. In addition, a risk factor is integrated into the structured analysis and design techniques (SADT) method forming a novel graphical view SADT-RISK; it identifies process’s failures using the traditional failure modes, effects and criticality analysis (FMECA) and economic consideration “failure mode and effect, criticality analysis-cost FMECA-C” making a multi-criterion matrix for better decision-making. Subsequently, some recommendations are proposed to overcome the failure. Findings This paper illustrates a methodology with a case study in a company, which has a leading brand in the market in Algeria. The authors are integrating a varied portfolio of approaches linking with each other to analyze, improve and optimize the processes in terms of reliability and safety to deal with risks; reduce the complexity of the systems; increase the performance; and achieve a safer process. However, the proposed method can be readily used in practice. Originality/value The paper provides a new approach based on integrated management using new elements as an innovative contribution, forming a novel graphical view SADT-RISK; it identifies process’s failures using the traditional FMECA and economic consideration “a new multi-criterion matrix for better decision-making and using the SWOT analysis – Strengths, Weaknesses, Opportunities, Threats – as a balance to decide about the process improvement”. The authors conclude that this methodology is oriented and applicable to different types of companies such as financial, health and industrial as illustrated by this case study.
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Liu, Min, Xifan Yao, Jianming Zhang, Wocheng Chen, Xuan Jing, and Kesai Wang. "Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations." Sensors 20, no. 17 (August 19, 2020): 4657. http://dx.doi.org/10.3390/s20174657.
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Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.
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Taimoor, Muhammad, Xiao Lu, Hamid Maqsood, and Chunyang Sheng. "Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle." Aircraft Engineering and Aerospace Technology 93, no. 5 (June 17, 2021): 847–61. http://dx.doi.org/10.1108/aeat-01-2021-0005.
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Purpose The objective of this research is to investigate various neural network (NN) observer techniques for sensors fault identification and diagnosis of nonlinear system in consideration of numerous faults, failures, uncertainties and disturbances. For the importunity of increasing the faults diagnosis and reconstruction preciseness, a new technique is used for modifying the weight parameters of NNs without enhancement of computational complexities. Design/methodology/approach Various techniques such as adaptive radial basis functions (ARBF), conventional radial basis functions, adaptive multi-layer perceptron, conventional multi-layer perceptron and extended state observer are presented. For increasing the fault detection preciseness, a new technique is used for updating the weight parameters of radial basis functions and multi-layer perceptron (MLP) without enhancement of computational complexities. Lyapunov stability theory and sliding-mode surface concepts are used for the weight-updating parameters. Based on the combination of these two concepts, the weight parameters of NNs are updated adaptively. The key purpose of utilization of adaptive weight is to enhance the detection of faults with high accuracy. Because of the online adaptation, the ARBF can detect various kinds of faults and failures such as simultaneous, incipient, intermittent and abrupt faults effectively. Results depict that the suggested algorithm (ARBF) demonstrates more confrontation to unknown disturbances, faults and system dynamics compared with other investigated techniques and techniques used in the literature. The proposed algorithms are investigated by the utilization of quadrotor unmanned aerial vehicle dynamics, which authenticate the efficiency of the suggested algorithm. Findings The proposed Lyapunov function theory and sliding-mode surface-based strategy are studied, which shows more efficiency to unknown faults, failures, uncertainties and disturbances compared with conventional approaches as well as techniques used in the literature. Practical implications For improvement of the system safety and for avoiding failure and damage, the rapid fault detection and isolation has a great significance; the proposed approaches in this research work guarantee the detection and reconstruction of unknown faults, which has a great significance for practical life. Originality/value In this research, two strategies such Lyapunov function theory and sliding-mode surface concept are used in combination for tuning the weight parameters of NNs adaptively. The main purpose of these strategies is the fault diagnosis and reconstruction with high accuracy in terms of shape as well as the magnitude of unknown faults. Results depict that the proposed strategy is more effective compared with techniques used in the literature.
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Li, Zhixiong, Yu Jiang, Xuping Wang, and Z. Peng. "Multi-mode separation and nonlinear feature extraction of hybrid gear failures in coal cutters using adaptive nonstationary vibration analysis." Nonlinear Dynamics 84, no. 1 (November 20, 2015): 295–310. http://dx.doi.org/10.1007/s11071-015-2505-3.
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Jin, Li, Guoan Zhang, Hao Zhu, and Wei Duan. "SDN-Based Survivability Analysis for V2I Communications." Sensors 20, no. 17 (August 19, 2020): 4678. http://dx.doi.org/10.3390/s20174678.
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In vehicle-to-infrastructure (V2I) communications, various failures in the dynamic movement pose serious link interruptions. To study the continuous service quality for the V2I network when this issue happens, this paper proposes a survivability analysis and establishes the communication architecture for software-defined network (SDN)-based V2I communications. With the controllable advantages of SDN centralized management, the multi-path transmission control protocol is used to seamlessly switch the transmission information between the V2I links of each vehicle node. Specifically, according to the analysis of specific fault types for V2I links, the definitions of SDN-based V2I survivability is provided to establish the corresponding survivability mode. To further verify the survivability model, a full-state search is adopted by means of probability model checker PRISM. In addition, multi-directional probability and expected reward evaluation analyses are carried out from the point of view of time. The simulation results show that, with the failure of multiple V2I links, the network quality of service (QoS) correspondingly declines, but the network still survives, due to the multi-path transmission control protocol (MPTCP) action. Moreover, with a high fault repair rate, the service performance and survivability of the network is improved rapidly.
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Ulfah, Maria, Ratna Ekawati, and Nafila Amalia. "Minimize the potential failures in the wire rod production process using six sigma and multi attribute failure mode analysis method." IOP Conference Series: Materials Science and Engineering 909 (December 22, 2020): 012058. http://dx.doi.org/10.1088/1757-899x/909/1/012058.
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Oh, Sukho, DongYeop Hwang, Kangseok Kim, and Ki-Hyung Kim. "A hybrid mode to enhance the downward route performance in routing protocol for low power and lossy networks." International Journal of Distributed Sensor Networks 14, no. 4 (April 2018): 155014771877253. http://dx.doi.org/10.1177/1550147718772533.
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An IPv6 routing protocol for low power and lossy networks provides an IPv6 communication for a wide range of applications in multi-hop mesh networks. The routing protocol for low power and lossy networks defines the creation and management of downward routes with two modes of operations: storing and non-storing modes. The storing and non-storing modes have weaknesses for memory constraints and packet traffic overheads, respectively. The storing mode may cause routing failures due to constraints on memory in routers and the non-storing mode may cause packet fragmentation that can become a factor for packet delays or loss. Then the problems may degrade the downward route performance in routing protocol for low power and lossy networks. Therefore, in this article, we propose a hybrid mode that combines the advantages of the existing two modes to improve the performance of downward packet transmission in routing protocol for low power and lossy networks networks. The proposed hybrid mode uses a new routing header format. The routing information for packet delivery is distributed with the extended routing header. We implement the proposed hybrid mode in Contiki OS environment to compare with existing techniques. From the experiment, it was observed that the proposed hybrid mode can improve the performance of downward packet transmission. Therefore, with the proposed hybrid mode, it is possible to configure a network enable to be composed of many leaf nodes with constrained memory. We also discuss future works.
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Li, Rui, Chao Ran, Bin Zhang, Leng Han, and Song Feng. "Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM." Applied Sciences 10, no. 16 (August 11, 2020): 5542. http://dx.doi.org/10.3390/app10165542.
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Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.
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Chang, Tai-Wu, Huai-Wei Lo, Kai-Ying Chen, and James Liou. "A Novel FMEA Model Based on Rough BWM and Rough TOPSIS-AL for Risk Assessment." Mathematics 7, no. 10 (September 20, 2019): 874. http://dx.doi.org/10.3390/math7100874.
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Failure mode and effects analysis (FMEA) is a risk assessment method that effectively diagnoses a product’s potential failure modes. It is based on expert experience and investigation to determine the potential failure modes of the system or product to develop improvement strategies to reduce the risk of failures. However, the traditional FMEA has many shortcomings that were proposed by many studies. This study proposes a hybrid FMEA and multi-attribute decision-making (MADM) model to establish an evaluation framework, combining the rough best worst method (R-BWM) and rough technique for order preference by similarity to an ideal solution technique (R-TOPSIS) to determine the improvement order of failure modes. In addition, this study adds the concept of aspiration level to R-TOPSIS technology (called R-TOPSIS-AL), which not only optimizes the reliability of the TOPSIS calculation program, but also obtains more potential information. This study then demonstrates the effectiveness and robustness of the proposed model through a multinational audio equipment manufacturing company. The results show that the proposed model can overcome many shortcomings of traditional FMEA, and effectively assist decision-makers and research and development (R&D) departments in improving the reliability of products.
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Appoh, Frederick, Akilu Yunusa-Kaltungo, Jyoti Kumar Sinha, and Moray Kidd. "Practical Demonstration of a Hybrid Model for Optimising the Reliability, Risk, and Maintenance of Rolling Stock Subsystem." Urban Rail Transit 7, no. 2 (May 11, 2021): 139–57. http://dx.doi.org/10.1007/s40864-021-00148-5.
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AbstractRailway transport system (RTS) failures exert enormous strain on end-users and operators owing to in-service reliability failure. Despite the extensive research on improving the reliability of RTS, such as signalling, tracks, and infrastructure, few attempts have been made to develop an effective optimisation model for improving the reliability, and maintenance of rolling stock subsystems. In this paper, a new hybrid model that integrates reliability, risk, and maintenance techniques is proposed to facilitate engineering failure and asset management decision analysis. The upstream segment of the model consists of risk and reliability techniques for bottom-up and top-down failure analysis using failure mode effects and criticality analysis and fault tree analysis, respectively. The downstream segment consists of a (1) decision-making grid (DMG) for the appropriate allocation of maintenance strategies using a decision map and (2) group decision-making analysis for selecting appropriate improvement options for subsystems allocated to the worst region of the DMG map using the multi-criteria pairwise comparison features of the analytical hierarchy process. The hybrid model was illustrated through a case study for replacing an unreliable pneumatic brake unit (PBU) using operational data from a UK-based train operator where the frequency of failures and delay minutes exceeded the operator’s original target by 300% and 900%, respectively. The results indicate that the novel hybrid model can effectively analyse and identify a new PBU subsystem that meets the operator’s reliability, risk, and maintenance requirements.
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Torres-Echeverria, A. C., and H. A. Thompson. "Multi-objective genetic algorithm for optimization of system safety and reliability based on IEC 61508 requirements: A practical approach." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 221, no. 3 (September 1, 2007): 193–205. http://dx.doi.org/10.1243/1748006xjrr85.
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This paper presents a practical approach for optimization by evolutionary computation of safety instrumented system design, based on safety and reliability measures, plus life cycle cost. The standard IEC 61508 establishes the necessity of this kind of systems to meet specific safety integrity requirements, expressed in terms of safety integrity levels (SIL). The SIL is determined in terms of average probability of failure on demand (PFDavg) for control systems that operate in demand mode. The optimization executed takes into account the level of modelling detail contemplated by the standard, including multiple failure modes, diagnostic coverage, and common cause failures. This study addresses the case of series-parallel systems. Optimization is approached by treating the problem as one of redundancy and reliability allocation, together with testing intervals specifications. Modelling is made through fault tree analysis with house events. The multi-objective genetic algorithm proposed by Fonseca and Fleming is used as the optimization technique.
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Araújo, Arthur Magno Medeiros de, Ana Beatriz do Nascimento Januário, Dayanne Monielle Duarte Moura, João Paulo Mendes Tribst, Mutlu Özcan, and Rodrigo Othávio Assunção Souza. "Can the Application of Multi-Mode Adhesive be a Substitute to Silicatized/Silanized Y-TZP Ceramics?" Brazilian Dental Journal 29, no. 3 (May 2018): 275–81. http://dx.doi.org/10.1590/0103-6440201801862.
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Abstract This study evaluated the effectiveness of a multi-mode adhesive (SBU-Scotch Bond Universal/3M) as a substitute for silica coating and silane application on the bonding of zirconia ceramics to resin cement. One-hundred and twenty sintered zirconia ceramic blocks (5 x 5 x 5 mm) were obtained, finished by grounding with silicon carbide paper (#600, #800, #1000 and #1200) and randomly divided into 12 groups (n=10) in accordance with the factors “surface treatment” (ScSi - silicatization + silanization; ScSBU - silicatization + SBU; SBU - SBU without photoactivation and SBUp - SBU photoactivated) and “ceramic” (Lava / 3M ESPE, Ceramill Zirconia / Amann Girrbach and Zirkonzahn / Zirkonzahn). Dual resin cement cylinders (RelyX Ultimate/3M ESPE) were subsequently produced in the center of each block using a silicon matrix (Ø=2 mm, h=5 mm) and photoactivated for 40 s (1200 mW/cm2). The samples were stored for 30 days in distilled water (37ºC) and submitted to shear bond strength test (1 mm/min, 100 KgF). Data (MPa) were analyzed under ANOVA (2 levels) and Tukey test (5%). Complementary analyzes were also performed. ANOVA revealed that only the factor “surface treatment” was significant (p=0.0001). The ScSi treatment (14.28A) promoted statistically higher bond strength values than the other ScSBU (9.03B), SBU (8.47B) and SBUp (7.82B), which were similar to each other (Tukey). Failure analysis revealed that 100% of the failures were mixed. The silica coating followed by the silanization promoted higher bond strength values of resin cement and ceramic, regardless of the zirconia ceramic or SBU.
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Ding, Siew-Hong, Shahrul Kamaruddin, and Ishak Abdul Azid. "Maintenance policy selection model – a case study in the palm oil industry." Journal of Manufacturing Technology Management 25, no. 3 (April 1, 2014): 415–35. http://dx.doi.org/10.1108/jmtm-03-2012-0032.
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Purpose – An optimal maintenance policy is key to the improvement of the availability and reliability of a system at an acceptable level without a significant increase in investment. However, the selection process is a complicated task because it requires in-depth knowledge on maintenance policies and on the technical requirements of maintenance. The difficulties and complexity of the selection process arise from the combination of conflicting maintenance constraints such as available spares, size of workforce, and maintenance skills. The paper aims to discuss these issues. Design/methodology/approach – The proposed maintenance policy selection (MPS) model is separated into three major phases. The first phase identifies the critical system (CS) based on failure frequency. The failure mechanism in the CS is then analyzed by using a failure mode and effect analysis in the second phase. In the third phase, a multi-criteria decision making method, called the technique for order of preference by similarity to ideal solution, is adopted to identify an optimal maintenance policy that can minimize the failures. Findings – Through a case study, preventive maintenance was selected as the optimal maintenance policy for the reduction of system failures. The results obtained from the case study not only provide evidence of the feasibility and practicability of the developed model, but also test the acceptability and rationale of the developed model from the industry perspective. Valuable knowledge and experience from employees were extracted and utilized through the proposed model to rank the optimal maintenance policy based on the capability to reduce failure. Originality/value – The practicality of the MPS model is justified through an implementation in the palm oil industry. The application of the MPS model can also be extended to other manufacturing industries.
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Jiang, Yu, Li Qin, Yuelei Zhang, and Jingping Wu. "Vibration Signal Processing for Gear Fault Diagnosis Based on Empirical Mode Decomposition and Nonlinear Blind Source Separation." Noise & Vibration Worldwide 42, no. 10 (November 2011): 55–61. http://dx.doi.org/10.1260/0957-4565.42.10.55.
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Gear failures happen frequently in the gear mechanisms, and an unexpected serious gear fault may cause severe damage on the machinery. Hence, precise gear fault detection at the early stage is imperative to ensure the normal operation of the machinery. Independent component analysis (ICA) has been paid more and more attention for its powerful ability of separating the useful vibration source from the multi-sensor observations to enhance the fault feature extraction. This is the so called blind source separation (BSS) procedure. However, the popular ICA model may suffer from two limitations. One is the linear mixture assumption, and the other is the lack of sensor channels. Up to now, only limited research considered the nonlinear ICA model in the field of mechanic fault diagnosis, and techniques for the situation where the number of sensor channels is less than the number of independent sources for gear defect detection are scarce. In order to extract the useful source involved with the gear fault characteristics in single-channel vibration signal processing, this work presents a new method based on the empirical mode decomposition (EMD) and nonlinear ICA. The EMD was firstly employed to decompose the vibration signal into a number of intrinsic mode functions (IMFs), and then these IMFs were taken as the multi-channel observations. The post-nonlinear (PNL) ICA model based on the radial basis function (RBF) neural network was applied to the nonlinear BSS procedure on the IMFs. The experimental vibration data acquired from the gear fault test-bed were processed for the validation of the proposed method. The nonlinear ICA method has been compared with the linear ICA and non-ICA based approaches. The analysis results show that the sensitive characteristics of the gear meshing vibration can be separated from the single channel measurement by the proposed method, and the fault diagnosis precision can be enhanced significantly. The detection rate can be increased by 3.75% or better when the ICA based preprocessing is carried out, and the proposed nonlinear ICA outperforms the linear ICA detection model.
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Vellucci, Sabrina. "An “Entirely Personal” Success: Intertextuality and Self-Reflexive Ironies in Henry James’s “Pandora”." Humanities 10, no. 2 (March 29, 2021): 61. http://dx.doi.org/10.3390/h10020061.
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Henry James’s self-allusions in “Pandora” have been read as a rewriting of his former treatment of the “American Girl abroad” in the comic mode. The hints at “a Tauchnitz novel by an American author” (90) establish an ironical reversal of the failures of understanding which had led to tragedy in “Daisy Miller.” Yet the ironies in “Pandora” are multi-layered, often self-reflexive, and can be further interpreted in the light of James’s controversial adaptation of his famous novella for the stage. In this framework, well-known Jamesian topoi appear both as a (self-)parody and a metaliterary dialogue James engages with his readers and critics. The author’s personal implication in this “American” story is further testified by his Notebooks, in which James states his intention to write about his friends Henry and “Clover” Adams. Indeed, “Pandora”’s multi-layered intertextuality includes undeclared references to Adams’s anonymously published novel, Democracy, a semi-satirical account of U.S. political life. My article focuses on the web of intertextual relations woven in this short story with a view to reflecting on James’s ideas concerning the politics of authorship, readership, literary success, and the fate of the American Girl.
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Oh, J., S. Poh, M. Ruzzene, and A. Baz. "Vibration Control of Beams Using Electro-Magnetic Compressional Damping Treatment." Journal of Vibration and Acoustics 122, no. 3 (January 1, 2000): 235–43. http://dx.doi.org/10.1115/1.1303004.
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A new class of structural damping treatments is introduced. This class is the electro-magnetic damping treatment (EMDT) which relies in its operation on a viscoelastic damping layer sandwiched between two magnetic layers. Interaction between the magnets generates magnetic forces that enhance the compressional damping mechanism of the viscoelastic layer. With proper tuning of the magnetic forces, in response to the structural vibration, undesirable resonances and catastrophic failures can be avoided. The fundamentals and the underlying phenomena associated with the EMDT are investigated theoretically and experimentally. A finite element model is developed to describe the interaction between the dynamics of flexible beams, the viscoelastic damping layer and the magnetic layers. The validity of the developed finite element model is checked experimentally using aluminum beams treated with EMDT patches. The beam/EMDT system is subjected to sinusoidal excitations and its multi-mode response is monitored when the magnetic layers are activated or not. Several control strategies are considered to activate the magnetic layers including simple PD controllers. The performance of the uncontrolled and controlled system is determined at various operating conditions. Attenuation of 49.4 percent is obtained for the amplitude of first mode of vibration (5.2 Hz) with control voltage of 0.2 volts. The attenuation increases to 72.56 percent for the second mode of vibration (28.6 Hz) with a control voltage of 1.68 volts. Comparisons with conventional Passive Constrained Layer Damping (PCLD) treatments emphasize the potential of the EMDT treatment as an effective means for controlling structural vibrations. [S0739-3717(00)00603-6]
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Wen, Wu, Yubao Liu, Rongfu Sun, and Yuewei Liu. "Research on Anomaly Detection of Wind Farm SCADA Wind Speed Data." Energies 15, no. 16 (August 12, 2022): 5869. http://dx.doi.org/10.3390/en15165869.
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Supervisory control and data acquisition (SCADA) systems are critical for wind power grid integration and wind farm operation and maintenance. However, wind turbines are affected by regulation, severe weather factors, and mechanical failures, resulting in abnormal SCADA data that seriously affect the usage of SCADA systems. Thus, strict and effective data quality control of the SCADA data are crucial. The traditional anomaly detection methods based on either “power curve” or statistical evaluation cannot comprehensively detect abnormal data. In this study, a multi-approach based abnormal data detection method for SCADA wind speed data quality control is developed. It is mainly composed of the EEMD (Ensemble Empirical Mode Decomposition)-BiLSTM network model, wind speed correlation between adjacent wind turbines, and the deviation detection model based on dynamic power curve fitting. The proposed abnormal data detection method is tested on SCADA data from a real wind farm, and statistical analysis of the results verifies that this method can effectively detect abnormal SCADA wind data. The proposed method can be readily applied for real-time operation to support an effective use of SCADA data for wind turbine control and wind power prediction.
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Diao, Yin, Jialun Pu, Hechuan Xu, and Rongjun Mu. "Orbit-Injection Strategy and Trajectory-Planning Method of the Launch Vehicle under Power Failure Conditions." Aerospace 9, no. 4 (April 7, 2022): 199. http://dx.doi.org/10.3390/aerospace9040199.
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Aiming at the problem of autonomous decision making and trajectory planning (ADMTP) for launch vehicles under power failure conditions, the target degradation order strategy (TDOS) and the trajectory online planning method were studied in this paper. Firstly, the influence of power failure on the orbit-injection process was analyzed. Secondly, the TDOS was proposed according to the mission attribute, failure mode, and multi-payload combination. Then, an online planning method based on the adaptive target update iterative guidance method (ATU-IGM) and radial basis neural network (RBFNN) was proposed, where the ATU-IGM adopted the basic TDOS criterion for generating optimal orbit-injection samples and online guidance instructions, and the RBFNN was used for orbit-injection samples training and online generation of orbital missions. Finally, the comparative simulation analysis was performed under multi-failure conditions. The results showed that the TDOS proposed in this paper could meet the requirements of the mission decision making under different failures, target orbit types, orbit-injection methods, and payload compositions. The online trajectory-planning capability deviation was less than 5%, and the mission decision-making and trajectory-planning time were less than 10 ms. This study provides theoretical support for autonomous decision making and planning of space launch missions.
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Lee, Thomas Y. S. "Analysis of Single Buffer Random Polling System With State-Dependent Input Process and Server/Station Breakdowns." International Journal of Operations Research and Information Systems 9, no. 1 (January 2018): 22–50. http://dx.doi.org/10.4018/ijoris.2018010102.
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Models and analytical techniques are developed to evaluate the performance of two variations of single buffers (conventional and buffer relaxation system) multiple queues system. In the conventional system, each queue can have at most one customer at any time and newly arriving customers find the buffer full are lost. In the buffer relaxation system, the queue being served may have two customers, while each of the other queues may have at most one customer. Thomas Y.S. Lee developed a state-dependent non-linear model of uncertainty for analyzing a random polling system with server breakdown/repair, multi-phase service, correlated input processes, and single buffers. The state-dependent non-linear model of uncertainty introduced in this paper allows us to incorporate correlated arrival processes where the customer arrival rate depends on the location of the server and/or the server's mode of operation into the polling model. The author allows the possibility that the server is unreliable. Specifically, when the server visits a queue, Lee assumes that the system is subject to two types of failures: queue-dependent, and general. General failures are observed upon server arrival at a queue. But there are two possibilities that a queue-dependent breakdown (if occurs) can be observed; (i) is observed immediately when it occurs and (ii) is observed only at the end of the current service. In both cases, a repair process is initiated immediately after the queue-dependent breakdown is observed. The author's model allows the possibility of the server breakdowns/repair process to be non-stationary in the number of breakdowns/repairs to reflect that breakdowns/repairs or customer processing may be progressively easier or harder, or that they follow a more general learning curve. Thomas Y.S. Lee will show that his model encompasses a variety of examples. He was able to perform both transient and steady state analysis. The steady state analysis allows us to compute several performance measures including the average customer waiting time, loss probability, throughput and mean cycle time.
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Hu, Ruo, Jinyang Feng, Zonglei Mou, Xunlong Yin, Zhenfei Li, and Hongrong Ma. "Incipient fault diagnosis for the cam-driven absolute gravimeter." Review of Scientific Instruments 93, no. 5 (May 1, 2022): 054501. http://dx.doi.org/10.1063/5.0079424.
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The vibration disturbance caused by incipient faults is an important factor affecting the measurement accuracy of the cam-driven absolute gravimeter. Based on the characteristics of the cam-driven absolute gravimeter, such as the small amplitude of the incipient faults, the inadequate representation of features for the faults, and hard-to-find in the noise, a novel method for incipient fault diagnosis of the cam-driven absolute gravimeter is put forward in this paper, which integrates the parameter-optimized Variational Mode Decomposition (VMD) with Light Gradient Boosting Machine (LightGBM). The sparrow search algorithm is used to optimize the VMD parameters. The parameter-optimized VMD algorithm is used to adaptively decompose the vibration signals of the gravimeter under different cases, and then an effective intrinsic mode function (IMF) is selected based on the Pearson correlation coefficient. Some high-frequency IMFs are subjected to adaptive noise reduction combined with low-frequency IMF reconstruction, and then the multi-scale permutation entropy with sensitive characteristics under different time scales is extracted as the fault feature vectors. The extracted multi-dimensional vector matrix is entered into the LightGBM classifier to realize the accurate diagnosis of the incipient faults for the cam-driven absolute gravimeter. The test results show that this method can effectively detect various incipient failures of the cam-driven absolute gravimeter, with an identification accuracy of 98.41%. With this method, the problem of low measurement accuracy for the cam-driven absolute gravimeter caused by the incipient faults is solved, and the rapid tracing and accurate positioning of these faults for the gravimeter are realized, promising a good prospect for engineering application.
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Wang, Zhen, Rongxi Wang, Wei Deng, and Yong Zhao. "An Integrated Approach-Based FMECA for Risk Assessment: Application to Offshore Wind Turbine Pitch System." Energies 15, no. 5 (March 3, 2022): 1858. http://dx.doi.org/10.3390/en15051858.
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Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts’ diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members’ diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis.
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Govindarajan, Rajaram, and Mohammed Laeequddin. "Failure mode and effect analysis (FMEA) of radiotherapy." Emerald Emerging Markets Case Studies 10, no. 4 (November 29, 2020): 1–22. http://dx.doi.org/10.1108/eemcs-10-2019-0281.
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Learning outcomes Learning outcomes are as follows: students will discover the importance of process orientation in management; students will determine the root cause of the problem by applying root cause analysis technique; students will identify the failure modes, analyze their effect, score them on a scale and prioritize the corrective action to prevent the failures; students will analyze the processes and propose error-proof system/s; and students will analyze organizational culture and ethical issues. Case overview/synopsis Purpose: This case study is intended as a class-exercise, for students to discover the importance of process-orientation in management, analyze the ethical dilemma in health care and to apply quality management techniques, such as five-why, root cause analysis, failure mode and effect analysis (FMEA) and error-proofing, in the management of the health-care and service industry. Design/methodology/approach: A voluntary reporting of a case of “radiation overdose” in a hospital’s radio therapy treatment unit, which led to an ethical dilemma. Consequently, a study was conducted to establish the causes of the incident and to develop a fail-proof system, to avoid recurrence. Findings: After careful analysis of the process-flow and the root causes, 25 potential failure modes were detected and the team had assigned a risk priority number (RPN) for each potential incident, selected the top ten RPNs and developed an error-proofing system to prevent recurrence. Subsequently, the improvement process was carried out for all the 25 potential incidents and a new control mechanism was implemented. The question of ethical dilemma remained unresolved. Research limitations/implications: Ishikawa diagram, FMEA and Poka-Yoke techniques require a multi-disciplinary team with process knowledge in identifying the possible root causes for errors, potential risks and also the possible error-proofing method/s. Besides, these techniques need frank discussions and agreement among team members on the efforts for the development of action plan, implementation and control of the new processes. Practical implications: Students can take the case data to identify root cause analysis and the RPN (RPN = possibility of detection × probability of occurrence × severity), to redesign the protocols, through systematic identification of the deficiencies of the existing protocols. Further, they can recommend quality improvement projects. Faculty can navigate the case session orientation, emphasizing quality management or ethical practices, depending on the course for which the case is selected. Complexity academic level MBA or PG Diploma in Management – health-care management, hospital administration, operations management, services operations, total quality management (TQM) and ethics. Supplementary materials Teaching Notes are available for educators only. Subject code CSS 9: Operations and Logistics.
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Taimoor, Muhammad, Xiao Lu, Wasif Shabbir, Chunyang Sheng, and Muhammad Samiuddin. "Novel neural observer based fault estimation, reconstruction and fault-tolerant control scheme for nonlinear systems." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 355–86. http://dx.doi.org/10.3233/jifs-201830.
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This research is concerned with the adaptive neural network observer based fault approximation and fault-tolerant control of time-varying nonlinear systems. A new strategy for adaptively updating the weights of neural network parameters is proposed to enhance fault detection accuracy. Lyapunov function theory (LFT) is applied for adaptively updating the learning parameters weights of multi-layer neural network (MLNN). The purpose of using adaptive learning rates to update the weight parameters of MLNN is to obtain the global minima for highly nonlinear functions without increasing the computational complexities and costs and increase the efficacy of fault detection. Results of the proposed adaptive MLNN observer are compared with conventional MLNN observer and high gain observer. The effects of various faults or failures are studied in detail. The proposed strategy shows more robustness to disturbances, uncertainties, and unmodelled system dynamics compared to the conventional neural network, high gain observer and other existing techniques in literature. Fault tolerant control (FTC) schemes are also proposed to account for the presence of various faults and failures. Separate sliding mode control (SMC) based FTC schemes are designed for each observer to ensure stability of the faulty system. The suggested strategy is validated on Boeing 747 100/200 aircraft. Results demonstrate the effectiveness of both the proposed adaptive MLNN observer and the FTC based on the proposed adaptive MLNN compared to the conventional MLNN, high gain observer and other existing schemes in literature. Comparison of the performance of all the strategies validates the superiority of the proposed strategy and shows that the FTC based on proposed adaptive MLNN strategy provides better robustness to various situations such as disturbances and uncertainties. It is concluded that the proposed strategy can be integrated into the aircraft for the purpose of fault diagnosis, fault isolation and FTC scheme for increasing the performance of the system.
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Harzelli, Imadeddine, Abdelhamid Benakcha, Tarek Ameid, and Arezki Menacer. "Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques." Journal of Modeling and Optimization 13, no. 2 (December 15, 2021): 58–79. http://dx.doi.org/10.32732/jmo.2021.13.2.58.
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In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.
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Liang, Xing, Yuanxing Luo, Fei Deng, and Yan Li. "Investigation on Vibration Signal Characteristics in a Centrifugal Pump Using EMD-LS-MFDFA." Processes 10, no. 6 (June 10, 2022): 1169. http://dx.doi.org/10.3390/pr10061169.
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Vibration signals from centrifugal pumps are nonlinear, non-smooth, and possess implied trend terms, which makes it difficult for traditional signal processing methods to accurately extract their fault characteristics and details. With a view to rectifying this, we introduced empirical mode decomposition (EMD) to extract the trend term signals. These were then refit using the least squares (LS) method. The result (EMD-LS) was then combined with multi-fractal theory to form a new signal identification method (EMD-LS-MFDFA), whose accuracy was verified with a binomial multi-fractal sequence (BMS). Then, based on the centrifugal pump test platform, the vibration signals of shell failures under different degrees of cavitation and separate states of loosened foot bolts were collected. The signals’ multi-fractal spectra parameters were analyzed using the EMD-LS-MFDFA method, from which five spectral parameters (Δα, Δf, α0, αmax, and αmin) were extracted for comparison and analysis. The results showed EMD-LS-MFDFA’s performance was closer to the BMS theoretical value than that of MFDFA, displayed high accuracy, and was fully capable of revealing the multiple fractal characteristics of the centrifugal pump fault vibration signal. Additionally, the mean values of the five types of multi-fractal spectral characteristic parameters it extracted were much greater than the normal state values. This indicates that the parameters could effectively distinguish the normal state and fault state of the centrifugal pump. Moreover, α0 and αmax had a smaller mean square than Δα, Δf and αmin, and their stability was higher. Thus, compared to the feature parameters extracted by MFDFA, our method could better realize the separation between the normal state, cavitation (whether slight, moderate, or severe), and when the anchor bolt was loose. This can be used to characterize centrifugal pump failure, quantify and characterize a pump’s different working states, and provide a meaningful reference for the diagnosis and study of pump faults.
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Zheng, Kai, and Xiangzhao Xu. "Experimental and Numerical Study on the Mechanical Behavior of Composite Steel Structure under Explosion Load." Materials 14, no. 2 (January 6, 2021): 246. http://dx.doi.org/10.3390/ma14020246.
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Most engineering structures are composed of basic components such as plates, shells, and beams, and their dynamic characteristics under explosion load determine the impact resistance of the structure. In this paper, a three-dimensional composite steel structure was designed using a beam, plate, and other basic elements to study its mechanical behavior under explosion load. Subsequently, experiments on the composite steel structure under explosion load were carried out to study its mechanical behavior, and the failure mode and deformation data of the composite steel structure were obtained, which provided important experimental data regarding the dynamic response and mechanical behavior of the composite steel structure under explosion load. Then, we independently developed a parallel program with the coupled calculation method to solve the numerical simulation of the dynamic response and failure process of the composite steel structure under explosion load. This program adopts the Euler method as a whole, and Lagrange particles are used for materials that need to be accurately tracked. The numerical calculation results are in good agreement with the experimental data, indicating that the developed parallel program can effectively deal with the large deformation problems of multi-medium materials and the numerical simulation of the complex engineering structure failures subjected to the strong impact load.
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Jain, Khushboo. "Use of failure mode effect analysis (FMEA) to improve medication management process." International Journal of Health Care Quality Assurance 30, no. 2 (March 13, 2017): 175–86. http://dx.doi.org/10.1108/ijhcqa-09-2015-0113.
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Purpose Medication management is a complex process, at high risk of error with life threatening consequences. The focus should be on devising strategies to avoid errors and make the process self-reliable by ensuring prevention of errors and/or error detection at subsequent stages. The purpose of this paper is to use failure mode effect analysis (FMEA), a systematic proactive tool, to identify the likelihood and the causes for the process to fail at various steps and prioritise them to devise risk reduction strategies to improve patient safety. Design/methodology/approach The study was designed as an observational analytical study of medication management process in the inpatient area of a multi-speciality hospital in Gurgaon, Haryana, India. A team was made to study the complex process of medication management in the hospital. FMEA tool was used. Corrective actions were developed based on the prioritised failure modes which were implemented and monitored. Findings The percentage distribution of medication errors as per the observation made by the team was found to be maximum of transcription errors (37 per cent) followed by administration errors (29 per cent) indicating the need to identify the causes and effects of their occurrence. In all, 11 failure modes were identified out of which major five were prioritised based on the risk priority number (RPN). The process was repeated after corrective actions were taken which resulted in about 40 per cent (average) and around 60 per cent reduction in the RPN of prioritised failure modes. Research limitations/implications FMEA is a time consuming process and requires a multidisciplinary team which has good understanding of the process being analysed. FMEA only helps in identifying the possibilities of a process to fail, it does not eliminate them, additional efforts are required to develop action plans and implement them. Frank discussion and agreement among the team members is required not only for successfully conducing FMEA but also for implementing the corrective actions. Practical implications FMEA is an effective proactive risk-assessment tool and is a continuous process which can be continued in phases. The corrective actions taken resulted in reduction in RPN, subjected to further evaluation and usage by others depending on the facility type. Originality/value The application of the tool helped the hospital in identifying failures in medication management process, thereby prioritising and correcting them leading to improvement.
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Mascia, A., A. M. Cirafici, A. Bongiovanni, G. Colotti, G. Lacerra, M. Di Carlo, F. A. Digilio, G. L. Liguori, A. Lanati, and A. Kisslinger. "A failure mode and effect analysis (FMEA)-based approach for risk assessment of scientific processes in non-regulated research laboratories." Accreditation and Quality Assurance 25, no. 5-6 (August 11, 2020): 311–21. http://dx.doi.org/10.1007/s00769-020-01441-9.
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AbstractNowadays, Quality Management tools such as failure mode and effect analysis (FMEA) are widely used throughout the aeronautical, automotive, software, food services, health care and many other industries to sustain and improve quality and safety. The increasing complexity of scientific research makes it more difficult to maintain all activities under control, in order to guarantee validity and reproducibility of results. Even in non-regulated research, scientists need to be supported with management tools that maximize study performance and outcomes, while facilitating the research process. Frequently, steps that involve human intervention are the weak links in the process. Risk analysis therefore gives considerable benefit to analytical validation, assessing and avoiding failures due to human error, potential imprecision in applying protocols, uncertainty in equipment function and imperfect control of materials. This paper describes in detail how FMEA methodology can be applied as a performance improvement tool in the field of non-regulated research, specifically on a basic Life Sciences research process. We chose as “pilot process” the selection of oligonucleotide aptamers for therapeutic purposes, as an example of a complex and multi-step process, suitable for technology transfer. We applied FMEA methodology, seeking every opportunity for error and its impact on process output, and then, a set of improvement actions was generated covering most aspects of laboratory practice, such as equipment management and staff training. We also propose a useful tool supporting the risk assessment of research processes and its outputs and that we named “FMEA strip worksheet.” These tools can help scientists working in non-regulated research to approach Quality Management and to perform risk evaluation of key scientific procedures and processes with the final aim to increase and better control efficiency and efficacy of their research.
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V. Saikumar, V., M. S.R. Rohith Reddy, Kumar Narayanan, C. Swaraj Paul, and R. Anandan. "Enhancement of the data fusion and sensor selection in cloud computing." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 313. http://dx.doi.org/10.14419/ijet.v7i2.21.12389.
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The IoT is helping individuals to get connected using sensible devices on the large function which is a big thing in past. The most difficult challenge for IoT is large quantity forgetting information generated from the induced devices that are less in number with resources and with missing information which results in the basic failures. By using IoT in collaboration with cloud, we have a function to present a cloud-based answer that takes into process that link quality and function to reduce energy usage by choosing sensors for sampling and dependent data. We have proposed a multi-phase adaptive sensing algorithm which shows belief propagation protocol, which may give high information quality and cut back energy usage by turning on mode with a little variety of nodes within the network. We have proposed a system which retrieves the data when the connection between device and cloud is lost. We will try then to use our message transferring rule for the proposed system. System is calculated support with the information collected from original elements. The basic function is whether maintaining is at the desired level of information quality and future accuracy will give large amount equalization in various sensing elements with success that stores about80% information within the compared object to other cases with all other area unit actively concerned.
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Somonggal, Hatian Ojak, and Santi Novani. "Decision Analysis to Find the Best Solution to Overcome Instrumentation Problems by Using Analytic Hierarchy Process and SMART Method." Journal of Integrated System 5, no. 2 (December 27, 2022): 123–42. http://dx.doi.org/10.28932/jis.v5i2.4710.
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The Laksmana offshore area is one of the oil field areas under Sumatera field. Laksmana offshore area produces 750 BOPD (Barrel Oil per Day). Instrumentation plays a vital role in the production process since it controls the Fluid Flow, level, and pressure. Based on the data collected from January 7th, 2020, to December 6th, 2020, there were 13 unplanned shutdowns caused by instrumentation system failures. This instrumentation systems failure can be grouped into three major categories: Air compressor failure, Instrumentation valve problems, and Instrument equipment malfunctions. Researchers attempted to stratify the problem using a Pareto diagram and find the root cause using a fishbone diagram and Failure Mode Effect Analysis. From the stratifying process, it was found that the company does not yet have any methods and facilities to monitor the condition of the compressor in real-time, so it is difficult to analyze and know the early signs of air compressor failure. The researcher conducted value-focused thinking with three members in a focus group discussion and generated four alternative solutions that can be used. The first alternative is installing the HMI (Human Machine Interface/SCADA) system. The second alternative is to purchase and use the S551 Data Logger. The third alternative is the assembly of own innovation tools made by the Laksmana’s worker, and the last is to appoint extra personnel for daily monitoring of the compressor. This research uses the Analytic Hierarchy Process (AHP) and Simple Multi-Attribute Rating Technique (SMART) methods to determine the best alternative and found that the own innovation tool is the best alternative.
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Edu, Abeeku Sam, Mary Agoyi, and Divine Agozie. "Digital security vulnerabilities and threats implications for financial institutions deploying digital technology platforms and application: FMEA and FTOPSIS analysis." PeerJ Computer Science 7 (August 3, 2021): e658. http://dx.doi.org/10.7717/peerj-cs.658.
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Digital disruptions have led to the integration of applications, platforms, and infrastructure. They assist in business operations, promoting open digital collaborations, and perhaps even the integration of the Internet of Things (IoTs), Big Data Analytics, and Cloud Computing to support data sourcing, data analytics, and storage synchronously on a single platform. Notwithstanding the benefits derived from digital technology integration (including IoTs, Big Data Analytics, and Cloud Computing), digital vulnerabilities and threats have become a more significant concern for users. We addressed these challenges from an information systems perspective and have noted that more research is needed identifying potential vulnerabilities and threats affecting the integration of IoTs, BDA and CC for data management. We conducted a step-by-step analysis of the potential vulnerabilities and threats affecting the integration of IoTs, Big Data Analytics, and Cloud Computing for data management. We combined multi-dimensional analysis, Failure Mode Effect Analysis, and Fuzzy Technique for Order of Preference by Similarity for Ideal Solution to evaluate and rank the potential vulnerabilities and threats. We surveyed 234 security experts from the banking industry with adequate knowledge in IoTs, Big Data Analytics, and Cloud Computing. Based on the closeness of the coefficients, we determined that insufficient use of backup electric generators, firewall protection failures, and no information security audits are high-ranking vulnerabilities and threats affecting integration. This study is an extension of discussions on the integration of digital applications and platforms for data management and the pervasive vulnerabilities and threats arising from that. A detailed review and classification of these threats and vulnerabilities are vital for sustaining businesses’ digital integration.
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Kaafarani, Rouba, Grace Abou-Jaoude, Joseph Wartman, and Miriam Tawk. "Landslide susceptibility mapping based on triggering factors using a multi-modal approach." MATEC Web of Conferences 281 (2019): 02002. http://dx.doi.org/10.1051/matecconf/201928102002.
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Landslide susceptibility mapping has been done using statistical and physically-based assessment techniques with limited focus on mode-specific models to identify failure modes and runout patterns. Because each failure mode has different consequences, it is essential to identify the failure mode associated with each slope inclination category, triggering factor, and geological setting. This paper presents a multimodal regionalscale assessment procedure for rainfall and earthquake-induced landslides, in the country of Lebanon, where landslide inventories are not available. Three failure modes are studied: debris flows, rock-slope failures, and coherent rotational slides. Areas prone to each mode of failure are identified based on geology and topography, then, using mode-specific models, their susceptibility to landslides is assessed. A runout assessment approach is then presented to identify the influence area of each predicted landslide and to obtain comprehensive susceptibility maps. Field assessment validated the proposed model which was in good agreement with actual slope failures across Lebanon. Therefore, the multimodal approach may be used to assess rainfall-induced landslide susceptibility, especially when landslide inventories are unavailable.
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Petrosoniak, A., M. Fan, P. Trbovich, K. White, S. Pinkney, M. McGowan, A. Gray, D. Campbell, S. Rizoli, and C. Hicks. "P103: A human factors-based framework analysis for patient safety: the trauma resuscitation using in situ simulation team training (TRUST) experience." CJEM 19, S1 (May 2017): S113. http://dx.doi.org/10.1017/cem.2017.305.
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Introduction: Effective trauma resuscitation requires a coordinated team approach, yet there is a significant risk for error. These errors can manifest from sequential system-, team- and knowledge based failures, defined as latent safety threats (LSTs). In situ simulation (ISS), a point-of-care training strategy, provides a novel prospective approach to identify factors that impact patient safety. This study quantified and formulated a hierarchy of LSTs during risk-informed ISS trauma resuscitations. Methods: At a Level 1 trauma centre, we conducted 12 multi-disciplinary, unannounced ISSs to prospectively identify trauma-related LSTs. Four, risk-informed scenarios were developed based on 5 recurring themes found within the trauma program’s morbidity and mortality process. The actual, on-call trauma team participated in the study. Simulations were video recorded with 4 cameras, each positioned at a different angle. Using a framework analysis methodology, human factors experts transcribed and coded the videos. Thematic structure was established deductively based on existing literature and inductively based on observed ISS events. All LSTs were prioritized for future patient safety, systems and ergonomic interventions using the Healthcare Failure Mode and Effect Analysis (HFMEA) matrix. Results: We identified 893 LSTs from 12 simulations. LST analysis resulted in 8 themes subcategorized into 43 codes. Themes were associated with team-, knowledge- or system-related issues. The following themes emerged: situational awareness, provider safety, mental model alignment, team/individual responsibility, team resources, equipment considerations, workplace environment and clinical protocols. The HFMEA hazard scoring process identified 13 high priority codes that required urgent attention and intervention to mitigate negative patient outcomes. Conclusion: A prospective, video-based framework analysis represents a novel and robust approach to LST identification within trauma care. Patterns of LSTs within and between simulations provide a high degree of transparency and traceability for an inter-professional trauma program review. Hazard matrix scoring facilitates the classification and prioritization of human factors interventions intended to improve patient safety.
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Alatyrev, S. S., I. S. Kruchinkina, A. S. Alatyrev, and A. P. Yurkin. "Simulation of the process of shipping and stowing cabbage in containers with gentle machine-harvesting regime of cabbage." Traktory i sel hozmashiny 84, no. 10 (October 15, 2017): 50–54. http://dx.doi.org/10.17816/0321-4443-66348.
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During traditional machine cleaning cabbages are mechanically damaged to a considerable extent. In this regard, a new method of machine cleaning is proposed, which allows to significantly reduce their damageability. In it, unlike the traditional method, the heads are first shipped in a gentle manner to a flexible trough floor, mounted on a special S rack on the vehicle platform, and then manually transferred into containers. The intensity of the process of shifting cabbage heads from the decking to the containers increases with the increase in the number of attendants on the back __ of the accompanying vehicle. However, the number of them should be as small as possible in order to reduce the labor _j costs for this operation. In addition, the number of jobs on the body of the accompanying vehicle is limited by its size. < For these reasons, the purpose of the study is to justify the required number of personnel involved in rearranging the С cabbage heads from the flooring into containers. In support of the number of personnel, this process is considered as s a multi-channel queuing system with failures. As a result, the essence of the occurring phenomena is described and ^ it is established with a quantitative enough connection for the practical accuracy between the characteristics of the i_ cabbage flow coming from the elevator to the flooring and the number of maintenance personnel shifting them into □_ containers. Modeling of the process of shipment and packing of cabbage in containers with sparing mode of machine О harvesting of cabbage based on the theory of mass service is carried out. The presented methodology and results of , calculations can be taken as a basis for justifying the number of personnel in the proposed method of machine har- < vesting of cabbage.
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CHEUNG, KI LING, and WARREN H. HAUSMAN. "Multiple failures in a multi-item spares inventory model." IIE Transactions 27, no. 2 (April 1995): 171–80. http://dx.doi.org/10.1080/07408179508936729.
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