Journal articles: 'Automatic fault analysis'

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Baek, Sujeong. "System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems." International Journal of Advanced Manufacturing Technology 113, no. 3-4 (January 29, 2021): 955–66. http://dx.doi.org/10.1007/s00170-021-06652-z.

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AbstractAs automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.

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Wu, Jiang, Zhuo Zhang, Jianjun Xu, Jiayu He, Xiaoguang Mao, Xiankai Meng, and Panpan Li. "Detraque: Dynamic execution tracing techniques for automatic fault localization of hardware design code." PLOS ONE 17, no. 9 (September 16, 2022): e0274515. http://dx.doi.org/10.1371/journal.pone.0274515.

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In an error-prone development process, the ability to localize faults is a crucial one. Generally speaking, detecting and repairing errant behavior at an early stage of the development cycle considerably reduces costs and development time. The debugging of the Verilog program takes much time to read the waveform and capture the signal, and in many cases, problem-solving relies heavily on experienced developers. Most existing Verilog fault localization methods utilize the static analysis method to find faults. However, using static analysis methods exclusively may result in some types of faults being inevitably ignored. The use of dynamic analysis could help resolve this issue. Accordingly, in this work, we propose a new fault localization approach for Verilog, named Detraque. After obtaining dynamic execution through test cases, Detraque traces these executions to localize faults; subsequently, it can determine the likelihood of any Verilog statement being faulty and sort the statements in descending order by suspicion score. Through conducting empirical research on real Verilog programs with 61 faulty versions, Detraque can achieve an EXAM score of 18.3%. Thus, Detraque is verified as able to improve Verilog fault localization effectiveness when used as a supplement to static analysis methods.

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Wang, Hong Li, Bing Xu, Xue Dong Xue, and Kan Cheng. "Application of Time-Frequency Analysis & Blind Source Separation to Diagnosis of Faults with Generator Rotor System." Applied Mechanics and Materials 556-562 (May 2014): 2748–51. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2748.

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One method for diagnosis of faults with generator rotor is contrived by combining local wave method and blind source separation. Time-frequency image varies with local wave of different fault signals, and this feature is applied to identify different faults. In order to realize automatic classification of faults, blind source separation is employed for separation of independent components in time-frequency image of local wave of different fault signals, so as to derive projection coefficients for a set of source images. On the basis of this, automatic classification of faults is realized with probability nerve network. Taking fault signal of rotor as an example, this method is investigated, and the validity is proved by experimental results.

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Gloaguen, R., P. R. Marpu, and I. Niemeyer. "Automatic extraction of faults and fractal analysis from remote sensing data." Nonlinear Processes in Geophysics 14, no. 2 (March 22, 2007): 131–38. http://dx.doi.org/10.5194/npg-14-131-2007.

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Abstract. Object-based classification is a promising technique for image classification. Unlike pixel-based methods, which only use the measured radiometric values, the object-based techniques can also use shape and context information of scene textures. These extra degrees of freedom provided by the objects allow the automatic identification of geological structures. In this article, we present an evaluation of object-based classification in the context of extraction of geological faults. Digital elevation models and radar data of an area near Lake Magadi (Kenya) have been processed. We then determine the statistics of the fault populations. The fractal dimensions of fault dimensions are similar to fractal dimensions directly measured on remote sensing images of the study area using power spectra (PSD) and variograms. These methods allow unbiased statistics of faults and help us to understand the evolution of the fault systems in extensional domains. Furthermore, the direct analysis of image texture is a good indicator of the fault statistics and allows us to classify the intensity and type of deformation. We propose that extensional fault networks can be modeled by iterative function system (IFS).

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Ran, Sheng Yi, and Yu Shu Xiong. "Research on Elimination of Electric Power System Fault Based on Electrical Engineering Automatic Control Technology." Advanced Materials Research 898 (February 2014): 771–74. http://dx.doi.org/10.4028/www.scientific.net/amr.898.771.

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In this paper we introduce the computer software fault analysis system to the power fault detection system, and design power fault elimination system of electrical engineering automatic control, and do simulation and experimental study on the performance. When using the turbine blade of electric machinery to detect fault, we can get the automated troubleshooting displacement curve, and using computer simulation to get the electric mechanical stress distribution nephogram. To further verify the effectiveness of the algorithm, we test the frequencies for eight different units, and obtain eight different sets of five order fault diagnosis frequency, and draw the frequency spectrum distribution of frequency response. It provides the theory reference for the automation of power system fault exclusion.

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Parzinger, Michael, Ulrich Wellisch, Lucia Hanfstaengl, Ferdinand Sigg, Markus Wirnsberger, and Uli Spindler. "Identifying faults in the building system based on model prediction and residuum analysis." E3S Web of Conferences 172 (2020): 22001. http://dx.doi.org/10.1051/e3sconf/202017222001.

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The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.

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Xu, Lei, Tiantian Wang, Jingsong Xie, Jinsong Yang, and Guangjun Gao. "A Mechanism-Based Automatic Fault Diagnosis Method for Gearboxes." Sensors 22, no. 23 (November 25, 2022): 9150. http://dx.doi.org/10.3390/s22239150.

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Convenient and fast fault diagnosis is the key to improving the service safety and maintenance efficiency of gearboxes. However, the environment and working conditions under complex service conditions are variable, and there is a lack of fault samples in engineering applications. These factors lead to difficulties in intelligent diagnosis methods based on machine learning, while traditional mechanism-based fault diagnosis requires high expertise and long time periods for the manual analysis of data. For the requirements of diagnostic convenience, an automatic fault diagnosis method for gearboxes is proposed in this paper. The method achieves accurate acquisition of rotational speed by constructing a rotational frequency search algorithm. The self-referencing characteristic frequency identification method is proposed to avoid manual signal analysis. On this basis, a framework of anti-interference automatic diagnosis is constructed to realize automatic diagnosis of gear faults. Finally, a gear fault experiment is carried out based on a high-fidelity experimental bench of bogie to verify the effectiveness of the proposed method. The proposed automatic diagnosis method does not rely on a large number of fault samples and avoids the need for diagnosis through professional knowledge, thus saving time for data analysis and promoting the application of fault diagnosis methods.

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Lu, Zhi Li, Shi Guang Hu, Tai Yong Wang, Dong Xiang Chen, and Qing Jian Liu. "Remote Monitoring and Intelligent Fault Diagnosis Technology Research Based on Open CNC System." Advanced Materials Research 819 (September 2013): 234–37. http://dx.doi.org/10.4028/www.scientific.net/amr.819.234.

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In modem manufacturing,the various and complex requirement of industry makes the CNC machine tools more and more automatic and networking.While a remote monitoring and intelligent fault diagnosis system is the basic and indispensable unit for automatic and networking machine tools.This paper is focused on open CNC system,the condition monitoring, and fault diagnosis technology are researched of open CNC system. Integration achieved the CNCmachine tools' status remote monitoring and intelligent fault diagnosis, and detailed analysis of the key technologies for the components of the system. Through effectively integration of the computer technology, Fault Tree Analysis method, or other technologies to enhance the automation, networking and intelligent level of the open CNC system. Keywords: open CNC system; remote monitoring; intelligent fault diagnosis ;Fault Tree Analysis Method;

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Yang, Yiping, Hongyan Wu, and Jianmin Ma. "Electrical System Design and Fault Analysis of Machine Tool Based on Automatic Control." International Journal of Automation Technology 15, no. 4 (July 5, 2021): 547–52. http://dx.doi.org/10.20965/ijat.2021.p0547.

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Automatically controlled machine tools have been used extensively in the industrial field, and fault analysis methods have garnered increasing attention. This paper first describes the software and hardware design of a machine tool and then presents a fault analysis of the machine tool. The fault types of machine tools are analyzed. A signal is obtained from a vibration sensor, the characteristic value is extracted, and the fault is analyzed using a back-propagation neural network (BPNN). The experimental results show that the BPNN yields the best performance when the structure is 8-9-8, and its recognition rate is 97.22% for different types of faults. Meanwhile, the recognition rate of naive Bayes is only 76.73%, and that of a support vector machine is only 85.55%, which is significantly lower than that of the BPNN. The results show that the BPNN is effective in fault analysis and can be promoted and applied more extensively.

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Jia, Zhi Cheng, Zhao Jun Yang, and Gui Xiang Shen. "Early Failure Mode Effect and Criticality Analysis for CNC Machining Tools." Applied Mechanics and Materials 303-306 (February 2013): 1653–56. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1653.

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According to the plentiful early failure data collected on the spots of 30 CNC machining tools with ATC (Automatic Tool Changer), FMECA method is used to analyze the main fault mode and facts which usually make a great possible impact on the reliability of these products during early failure period.We find out the fault positions and the ratios of the fault modes and fault causes. We further analyze the fault modes and fault causes which frequently arise in the subsystems and parts. Through the analyses of FMECA, we learn the weak links of CNC machining tools and provide bases for removing the products faults. Suggestions how to enhance reliability level of these products are proposed.

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Pearman, Gordon R. "Automatic BusBar Transfer Switch Fault Tree Analysis." Marine Technology and SNAME News 34, no. 01 (January 1, 1997): 31–43. http://dx.doi.org/10.5957/mt1.1997.34.1.31.

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This paper discusses the application of an existing statistical analysis technique called "Fault Tree Analysis " to Automatic BusBar Transfer Switch failures on a new class of Naval Supply Ship. Fault Tree Analysis is a technique, used in many "High Technology Industries, " to pictorially display facts surrounding system, equipment or process failures. Technological complexity and safety concerns in the marine industry make it necessary to also use an organized technique to display facts affecting undesirable events, or failures, and their cause and effect.

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Harris, J. "On the correlation of statistical and automatic process control." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 217, no. 1 (January 1, 2003): 99–109. http://dx.doi.org/10.1243/095440503762502314.

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A process control strategy is proposed based upon the twin themes of statistical and automatic process control. The main categories of product fault are identified and related to the capabilities of statistical and automatic control. Statistical control is supported by process fault information from a process-specific fault tree analysis, which provides the basis for a corrective intervention protocol. Application is discussed in terms of fuzzy automatic control, which offers a greater generality than conventional automatic control modelling. Prior publications that fuzzify statistical control zones are arguably incomplete in the application of logic propositions and also in the identification of process faults. The present work proposes a general strategy, which may be adapted to specific processes. Both control by variables and control by attributes may be included within this treatment.

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Yang, Zhenyu. "Automatic Condition Monitoring of Industrial Rolling-Element Bearings Using Motor’s Vibration and Current Analysis." Shock and Vibration 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/486159.

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An automatic condition monitoring for a class of industrial rolling-element bearings is developed based on the vibration as well as stator current analysis. The considered fault scenarios include a single-point defect, multiple-point defects, and a type of distributed defect. Motivated by the potential commercialization, the developed system is promoted mainly using off-the-shelf techniques, that is, the high-frequency resonance technique with envelope detection and the average of short-time Fourier transform. In order to test the flexibility and robustness, the monitoring performance is extensively studied under diverse operating conditions: different sensor locations, motor speeds, loading conditions, and data samples from different time segments. The experimental results showed the powerful capability of vibration analysis in the bearing point defect fault diagnosis. The current analysis also showed a moderate capability in diagnosis of point defect faults depending on the type of fault, severity of the fault, and the operational condition. The temporal feature indicated a feasibility to detect generalized roughness fault. The practical issues, such as deviations of predicted characteristic frequencies, sideband effects, time-average of spectra, and selection of fault index and thresholds, are also discussed. The experimental work shows a huge potential to use some simple methods for successful diagnosis of industrial bearing systems.

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Grzechca, Damian, Paweł Rybka, and Roman Pawełczyk. "Level Crossing Barrier Machine Faults and Anomaly Detection with the Use of Motor Current Waveform Analysis." Energies 14, no. 11 (May 31, 2021): 3206. http://dx.doi.org/10.3390/en14113206.

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Barrier machines are a key component of automatic level crossing systems ensuring safety on railroad crossings. Their failure results not only in delayed railway transportation, but also puts human life at risk. To prevent faults in this critical safety element of automatic level crossing systems, it is recommended that fault and anomaly detection algorithms be implemented. Both algorithms are important in terms of safety (information on whether a barrier boom has been lifted/lowered as required) and predictive maintenance (information about the condition of the mechanical components). Here, the authors propose fault models for barrier machine fault and anomaly detection procedures based on current waveform observation. Several algorithms were applied and then assessed such as self-organising maps (SOM), autoencoder artificial neural network, local outlier factor (LOF) and isolation forest. The advantage of the proposed solution is there is no change of hardware, which is already homologated, and the use of the existing sensors (in a current measurement module). The methods under evaluation demonstrated acceptable rates of detection accuracy of the simulated faults, thereby enabling a practical application at the test stage.

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Chen, Qi, Jincheng Wang, and Qadeer Ahmed. "Design and Evaluation of a Structural Analysis-Based Fault Detection and Identification Scheme for a Hydraulic Torque Converter." Sensors 18, no. 12 (November 23, 2018): 4103. http://dx.doi.org/10.3390/s18124103.

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A hydraulic torque converter (HTC) is a key component in an automatic transmission. To monitor its operating status and to detect and locate faults, and considering the high-efficiency fault detection and identification (FDI) scheme design by the methodology of structural analysis (SA), this paper presents an SA-based FDI system design and validation for the HTC. By the technique of fault mode and effect analysis (FMEA), eight critical faults are obtained, and then two fault variables are chosen to delegate them. Fault detectability and isolability, coupled with different sensor placements, are analyzed, and as a result, two speed sensors and two torque sensors of pump and turbine are selected to realize the maximal fault detectability and fault isolability: all six faults are detectable, four faults are uniquely isolable, and two faults are isolated from the other faults, but not from each other. Then five minimal structurally overdetermined (MSO) sets are easily acquired by SA to generate five corresponding residuals. The proposed FDI scheme of the HTC by SA is first validated by a theoretical model, then by an offline experiment in a commercial SUV, and the testing results indicate a consistent conclusion with the simulations and theory analysis.

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Cai, Gui Fang. "Design of a Virtual Automatic Test System of the Missile Autopilot." Applied Mechanics and Materials 543-547 (March 2014): 1377–80. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1377.

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To improve the test speed and efficiency, a virtual automatic test system of missile autopilot is designed. The inbuilt computer is used as the core of the system; the modularization instrument bus standard which composed of the PXI card, the programmable instrument and other advanced test techniques is adopted. The automation test device hardware facility is used in the virtual automatic test system; the pilot mechanism fault analysis software is developed, so the faults of the missile autopilot can be located faster and more accurate. The virtual automatic test system can be used to test the current parameters, special capability and the function of the electronic parts among the missile weapon system.

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Huang, Jie, Xinqing Wang, Dong Wang, Zhiwei Wang, and Xia Hua. "Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network." Entropy 21, no. 4 (April 22, 2019): 425. http://dx.doi.org/10.3390/e21040425.

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With the aim of automatic recognition of weak faults in hydraulic systems, this paper proposes an identification method based on multi-scale permutation entropy feature extraction of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN). In this method, the leakage fault signal is first decomposed by empirical mode decomposition (EMD), and fault-sensitive IMF components are screened by adopting the correlation analysis method. The multi-scale entropy feature of each screened IMF is then extracted and features closely related to the weak fault information are then obtained. Finally, DBN is used for identification of fault diagnosis. Experimental results prove that this identification method has an ideal recognition effect. It can accurately judge whether there is a leakage fault, determine the degree of severity of the fault, and can diagnose and analyze hydraulic weak faults in general.

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Dehestani, Davood, Jafar Madadnia, Homa Koosha, and Fahimeh Eftekhari. "Comprehensive Analysis for Air Supply Fan Faults Based on HVAC Mathematical Model." Advanced Materials Research 452-453 (January 2012): 460–68. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.460.

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Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems

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Hu, Ye, Zhao Jun Yang, Xiao Ming Zeng, Peng Fei Song, Yu Peng Ma, Yang Wang, and Qiao Lou. "Fault Detection System and Reliability Analysis for Chain-Type Tool Magazine." Advanced Materials Research 542-543 (June 2012): 941–44. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.941.

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By analyzing the structure features and tool changing process of a large-scale national Automatic Tool Changer (ATC) with a chain-type tool magazine, a chain-type tool magazine reliability test bench is designed with a new fault detection system, where a new failure data collecting method is applied for taking failure data statistics and reliability analysis. This set of fault detecting and reliability analyzing system not only provides a way for ATC to predict system faults, but also creates good conditions for further ATC reliability research.

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Rong, Haina, Kang Yi, Gexiang Zhang, Jianping Dong, Prithwineel Paul, and Zhiwei Huang. "Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems." Complexity 2019 (June 19, 2019): 1–16. http://dx.doi.org/10.1155/2019/2635714.

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As an important variant of membrane computing models, fuzzy reasoning spiking neural P systems (FRSN P systems) were introduced to build a link between P systems and fault diagnosis applications. An FRSN P system offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. However, the implementation of FRSN P systems is still at a manual process, which is a time-consuming and hard labor work, especially impossible to perform on large-scale complex power systems. This manual process seriously limits the use of FRSN P systems to diagnose faults in large-scale complex power systems and has always been a challenging and ongoing task for many years. In this work we develop an automatic implementation method for automatically fulfilling the hard task, named membrane computing fault diagnosis (MCFD) method. This is a very significant attempt in the development of FRSN P systems and even of the membrane computing applications. MCFD is realized by automating input and output, and diagnosis processes consists of network topology analysis, suspicious fault component analysis, construction of FRSN P systems for suspicious fault components, and fuzzy inference. Also, the feasibility of the FRSN P system is verified on the IEEE14, IEEE 39, and IEEE 118 node systems.

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Trothe, Max Emil S., Hamid Reza Shaker, Muhyiddine Jradi, and Krzysztof Arendt. "Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings." Energies 12, no. 9 (April 26, 2019): 1601. http://dx.doi.org/10.3390/en12091601.

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Faults and anomalies in buildings are among the main causes of building energy waste and occupant discomfort. An effective automatic fault detection and diagnosis (FDD) process in buildings can therefore save a significant amount of energy and improve the comfort level. Fault diagnosability analysis and an optimal FDD-oriented sensor placement are prerequisites for effective, efficient and successful diagnostics. This paper addresses the problem of fault diagnosability for smart buildings. The method used in the paper is a model-based technique which uses Dulmage-Mendelsohn decomposition. To the best of our knowledge, this is the first time that this method is used for applications in smart buildings. First a dynamic model for a zone in a real-case building is developed in which faults are also introduced. Then fault diagnosability is investigated by analyzing the fault isolability of the model. Based on the investigation, it was concluded that not all the faults in the model are diagnosable. Then an approach for placing new sensors is implemented. It is observed that for two test scenarios, placing additional sensors in the model leads to full diagnosability. Since sensors placement is key for an effective FDD process, the optimal placement of such sensors is also studied in this work. A case study of campus building OU44 at the University of Southern Denmark is considered. The results show that as the system gets more complicated by introducing more faults, additional sensors should be added to achieve full diagnosability.

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Xiufen, Chen. "Automatic zero sequence fault monitoring and diagnosis analysis." Research on Smart Grid 2, no. 1 (2020): 8–16. http://dx.doi.org/10.35534/rsg.0201002c.

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Ma, Siqi, Xin Wang, Xiaochen Wang, Hanyu Liu, and Runtong Zhang. "A Framework for Diagnosing Urban Rail Train Turn-Back Faults Based on Rules and Algorithms." Applied Sciences 11, no. 8 (April 8, 2021): 3347. http://dx.doi.org/10.3390/app11083347.

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Although urban rail transit provides significant daily assistance to users, traffic risk remains. Turn-back faults are a common cause of traffic accidents. To address turn-back faults, machines are able to learn the complicated and detailed rules of the train’s internal communication codes, and engineers must understand simple external features for quick judgment. Focusing on turn-back faults in urban rail, in this study we took advantage of related accumulated data to improve algorithmic and human diagnosis of this kind of fault. In detail, we first designed a novel framework combining rules and algorithms to help humans and machines understand the fault characteristics and collaborate in fault diagnosis, including determining the category to which the turn-back fault belongs, and identifying the simple and complicated judgment rules involved. Then, we established a dataset including tabular and text data for real application scenarios and carried out corresponding analysis of fault rule generation, diagnostic classification, and topic modeling. Finally, we present the fault characteristics under the proposed framework. Qualitative and quantitative experiments were performed to evaluate the proposed method, and the experimental results show that (1) the framework is helpful in understanding the faults of trains that occur in three types of turn-back: automatic turn-back (ATB), automatic end change (AEC), and point mode end change (PEC); (2) our proposed framework can assist in diagnosing turn-back faults.

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Liu, Yan, Jing Lei, Shasha Zeng, Yajie Wang, and Zhaohua Nian. "Design of satellite ground fault diagnosis system based on rule base." ITM Web of Conferences 47 (2022): 01013. http://dx.doi.org/10.1051/itmconf/20224701013.

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The automation and intelligentization of fault diagnosis system for satellite ground system directly affect the success of tasks and the reliability of the system. This paper designed a fault diagnosis system based on rule base, which contained satellite ground system failure rule base, failure model, abnormal and alarm mechanism. Software implementation has been verified by actual project, it shows that the fault diagnosis system based on rule base can improve the capacity of fault management functions, real-time monitoring and automatic fault diagnosis support system. In addition, fault analysis and location can enhance the automation level and efficiency of satellite fault diagnosis, make efficient and reliable diagnosis of remote sensing satellite receiving system, raise the success rate of satellite data receiving, and have good practicability and popularization.

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Ouanas, Ali, Ammar Medoued, Salim Haddad, Mourad Mordjaoui, and D. Sayad. "Automatic and Online Detection of Rotor Fault State." International Journal of Renewable Energy Development 7, no. 1 (February 18, 2018): 43. http://dx.doi.org/10.14710/ijred.7.1.43-52.

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In this work, we propose a new and simple method to insure an online and automatic detection of faults that affect induction motor rotors. Induction motors now occupy an important place in the industrial environment and cover an extremely wide range of applications. They require a system installation that monitors the motor state to suit the operating conditions for a given application. The proposed method is based on the consideration of the spectrum of the single-phase stator current envelope as input of the detection algorithm. The characteristics related to the broken bar fault in the frequency domain extracted from the Hilbert Transform is used to estimate the fault severity for different load levels through classification tools. The frequency analysis of the envelope gives the frequency component and the associated amplitude which define the existence of the fault. The clustering of the indicator is chosen in a two-dimensional space by the fuzzy c mean clustering to find the center of each class. The distance criterion, the K-Nearest Neighbor (KNN) algorithm and the neural networks are used to determine the fault type. This method is validated on a 5.5-kW induction motor test bench.Article History: Received July 16th 2017; Received: October 5th 2017; Accepted: Januari 6th 2018; Available onlineHow to Cite This Article: Ouanas, A., Medoued, A., Haddad, S., Mordjaoui, M., and Sayad, D. (2017) Automatic and online Detection of Rotor Fault State. International Journal of Renewable Energy Development, 7(1), 43-52.http://dx.doi.org/10.14710/ijred.7.1.43-52

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Chen, Zuotian, Xiaodong He, Quan Ge, and Yang Zhou. "Fault analysis and troubleshooting of nitrogen supply system for a ship’s nitrogen generator." Journal of Physics: Conference Series 2403, no. 1 (December 1, 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2403/1/012029.

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Abstract This paper introduces the working principle and system work flow of the marine nitrogen generator. Emphasis is laid on the air pretreatment subsystem, nitrogen membrane separation subsystem and automatic control system of the nitrogen generator. In view of the automatic alarm shutdown fault of a ship’s nitrogen generator in the course of operation, the reason is analyzed, and it is found that the refrigeration device fault of the nitrogen generator leads to the excessive air temperature before the film inlet group, thus causing the failure shutdown. Finally, the fault location and troubleshooting are carried out.

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Paul, Satyam, Rob Turnbull, Davood Khodadad, and Magnus Löfstrand. "A Vibration Based Automatic Fault Detection Scheme for Drilling Process Using Type-2 Fuzzy Logic." Algorithms 15, no. 8 (August 12, 2022): 284. http://dx.doi.org/10.3390/a15080284.

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The fault detection system using automated concepts is a crucial aspect of the industrial process. The automated system can contribute efficiently in minimizing equipment downtime therefore improving the production process cost. This paper highlights a novel model based fault detection (FD) approach combined with an interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy system for fault detection in the drilling process. The system uncertainty is considered prevailing during the process, and type-2 fuzzy methodology is utilized to deal with these uncertainties in an effective way. Two theorems are developed; Theorem 1, which proves the stability of the fuzzy modeling, and Theorem 2, which establishes the fault detector algorithm stability. A Lyapunov stabilty analysis is implemented for validating the stability criterion for Theorem 1 and Theorem 2. In order to validate the effective implementation of the complex theoretical approach, a numerical analysis is carried out at the end. The proposed methodology can be implemented in real time to detect faults in the drilling tool maintaining the stability of the proposed fault detection estimator. This is critical for increasing the productivity and quality of the machining process, and it also helps improve the surface finish of the work piece satisfying the customer needs and expectations.

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Xu, Peng, Ahmad Ghasemloonia, and Qiao Sun. "Automatic band selection algorithm for envelope analysis." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 5 (May 29, 2018): 1641–54. http://dx.doi.org/10.1177/0954406218776342.

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Envelope analysis has been widely used to detect early stage faults of rolling element bearings. The primary initial step of envelope analysis is the proper selection of the resonance band for demodulation. Current band selection methods, such as wide band selection, “power spectral density” comparison, and selecting the accelerometer resonance band have limitations such as disturbance of the wide band, the need for a healthy signal for comparison, and the implementation of specialized sensors. In this study, an enhanced method of resonance band selection for envelope analysis was developed. The developed method implements high-pass filtering and “time synchronous averaging” to remove dominant speed-dependent (nonsynchronous and synchronous) spectral contents of a vibration signal. Wavelet packet transform and “root mean square” were then applied to determine the energy distribution of the residual signal. The band with the highest energy (resonance band) was selected for envelope analysis. An experimental study was designed for cross-validation of the developed method. The developed method in this study is more practical than current band selection methods and has no special requirement for sensors. The developed algorithm can be implemented as a processing algorithm in a commercial vibration analyzer, which enhances its ability in early-stage bearing fault detection.

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Singh, Dimpy, Akshada Deshpande, Ishika Sandilya, Anupriya Lakra, Annpurna Tandan, and Mousam Sharma. "Three Phase Fault Analysis with Automatic Trip and Reclosing." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 711–15. http://dx.doi.org/10.26438/ijcse/v7i4.711715.

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Lunglmayr, Michael, and Gustavo C. Amaral. "Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis." IEEE Transactions on Instrumentation and Measurement 68, no. 10 (October 2019): 3699–711. http://dx.doi.org/10.1109/tim.2018.2882258.

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Merainani, Boualem, Chemseddine Rahmoune, Djamel Benazzouz, and Belkacem Ould-Bouamama. "A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network." Journal of Vibration and Control 24, no. 12 (February 1, 2017): 2512–31. http://dx.doi.org/10.1177/1077546316688991.

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There are growing demands for condition monitoring and fault diagnosis of rotating machinery to lower unscheduled breakdown. Gearboxes are one of the fundamental components of rotating machinery; their faults identification and classification always draw a lot of attention. However, non-stationary vibration signals and low energy of weak faults makes this task challenging in many cases. Thus, a new fault diagnosis method which combines the Hilbert empirical wavelet transform (HEWT), singular value decomposition (SVD), and self-organizing feature map (SOM) neural network is proposed in this paper. HEWT, a new self-adaptive time-frequency analysis was applied to the vibration signals to obtain the instantaneous amplitude matrices. Then, the singular value vectors, as the fault feature vectors were acquired by applying the SVD. Last, the SOM was used for automatic gearbox fault identification and classification. An electromechanical model comprising an induction motor coupled with a single stage spur gearbox is considered where the vibration signals of four typical operation modes were simulated. The conditions include the healthy gearbox, input shaft slant crack, tooth cracking, and tooth surface pitting. Obtained results show that the proposed method effectively identifies the gearbox faults at an early stage and realizes automatic fault diagnosis. Moreover, performance evaluation and comparison between the proposed HEWT–SVD method and Hilbert–Huang transform (HHT)–SVD approach show that the HEWT–SVD is better for feature extraction.

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Buzzoni, M., E. Mucchi, G. D’Elia, and G. Dalpiaz. "Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm." Shock and Vibration 2017 (2017): 1–22. http://dx.doi.org/10.1155/2017/8345704.

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The gear fault diagnosis on multistage gearboxes by vibration analysis is a challenging task due to the complexity of the vibration signal. The localization of the gear fault occurring in a wheel located in the intermediate shaft can be particularly complex due to the superposition of the vibration signature of the synchronous wheels. Indeed, the gear fault detection is commonly restricted to the identification of the stage containing the faulty gear rather than the faulty gear itself. In this context, the paper advances a methodology which combines the Empirical Mode Decomposition and the Time Synchronous Average in order to separate the vibration signals of the synchronous gears mounted on the same shaft. The physical meaningful modes are selected by means of a criterion based on Pearson’s coefficients and the fault detection is performed by dedicated condition indicators. The proposed method is validated taking into account simulated vibrations signals and real ones.

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Chen, Jun, and Shu Zhong Lin. "The Research of Button Battery Automatic Production Line Remote Fault Analysis." Applied Mechanics and Materials 271-272 (December 2012): 1684–88. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1684.

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This article unifies the Li/Mno2button battery automatic production line of production practice, using C/S two-layer system with the B/S three layer architecture combines database system model, mainly expounds the production line in remote fault diagnosis system of fault data analysis and diagnosis method and so on.

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Huerta-Rosales, Jose R., David Granados-Lieberman, Juan P. Amezquita-Sanchez, David Camarena-Martinez, and Martin Valtierra-Rodriguez. "Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach." Mathematics 8, no. 4 (April 13, 2020): 575. http://dx.doi.org/10.3390/math8040575.

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Transformers are vital and indispensable elements in electrical systems, and therefore, their correct operation is fundamental; despite being robust electrical machines, they are susceptible to present different types of faults during their service life. Although there are different faults, the fault of short-circuited turns (SCTs) has attracted the interest of many researchers around the world since the windings in a transformer are one of the most vulnerable parts. In this regard, several works in literature have analyzed the vibration signals that generate a transformer as a source of information to carry out fault diagnosis; however this analysis is not an easy task since the information associated with the fault is embedded in high level noise. This problem becomes more difficult when low levels of fault severity are considered. In this work, as the main contribution, the nonlinear mode decomposition (NMD) method is investigated as a potential signal processing technique to extract features from vibration signals, and thus, detect SCTs in transformers, even in early stages, i.e., low levels of fault severity. Also, the instantaneous root mean square (RMS) value computed using the Hilbert transform is proposed as a fault indicator, demonstrating to be sensitive to fault severity. Finally, a fuzzy logic system is developed for automatic fault diagnosis. To test the proposal, a modified transformer representing diverse levels of SCTs is used. These levels consist of 0 (healthy condition), 5, 10, 15, 20, and 25 SCTs. Results demonstrate the capability of the proposal to extract features from vibration signals and perform automatic fault diagnosis.

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Lee, Dongwoo, Dongmin Lee, and Jongwhoa Na. "Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model." Applied Sciences 12, no. 12 (June 16, 2022): 6144. http://dx.doi.org/10.3390/app12126144.

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In the development of safety-critical systems, it is important to perform failure modes and effects analysis (FMEA) to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an automatic FMEA tool that integrates the model-based design (MBD), FMEA, and simulated fault injection techniques in a single environment. The automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool: First, the automatic FMEA tool automatically generates FMEA reports, unlike the traditional spreadsheet-based FMEA tools. Second, the automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the automatic FMEA were compared to those of the legacy FMEA.

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Sangaiah, Arun Kumar, Samira Rezaei, Amir Javadpour, Farimasadat Miri, Weizhe Zhang, and Desheng Wang. "Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management." Algorithms 15, no. 11 (November 17, 2022): 432. http://dx.doi.org/10.3390/a15110432.

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Handling faults in a running cellular network can impair the performance and dissatisfy the end users. It is important to design an automatic self-healing procedure to not only detect the active faults, but also to diagnosis them automatically. Although fault detection has been well studied in the literature, fewer studies have targeted the more complicated task of diagnosing. Our presented method aims to tackle fault detection and diagnosis using two sets of data collected by the network: performance support system data and drive test data. Although performance support system data is collected automatically by the network, drive test data are collected manually in three mode call scenarios: short, long and idle. The short call can identify faults in a call setup, the long call is designed to identify handover failures and call interruption, and, finally, the idle mode is designed to understand the characteristics of the standard signal in the network. We have applied unsupervised learning, along with various classified algorithms, on performance support system data. Congestion and failures in TCH assignments are a few examples of the detected and diagnosed faults with our method. In addition, we present a framework to identify the need for handovers. The Silhouette coefficient is used to evaluate the quality of the unsupervised learning approach. We achieved an accuracy of 96.86% with the dynamic neural network method.

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Li, Peng, Fang Yi Che, Yi Min Qian, and Hai Bo Zhang. "System Design and Implementation of Distribution Automation Evaluation and Analysis System." Applied Mechanics and Materials 738-739 (March 2015): 1053–60. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.1053.

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In this paper, based on the requirements of standards system of distribution automation, standardized testing method and testing process of performance index of distribution automation system (DAS) is proposed according to the characteristics of implementation, and the technology of automatic test is studied. The paper also brings forward a set of feasible evaluation method and system of DAS to ensure the safe and stable running of distribution automation (DA), combining with a common information model (CIM) import and export function test based on feedback principle and a function test of feeder automation based on predefined fault model.

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Hardy, N. W., D. P. Barnes, and M. H. Lee. "Automatic diagnosis of task faults in flexible manufacturing systems." Robotica 7, no. 1 (January 1989): 25–35. http://dx.doi.org/10.1017/s0263574700005002.

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SUMMARYThe need for fault tolerant mechanisms in flexible manufacturing systems is described and previous work on diagnosis in robotics and other areas is considered. Fundamental difficulties in the analysis of robot cell malfunctions are described and a glossary of terms useful in this area is presented. Limited observational data on the occurrence of faults in assemblies are reported. Finally a proposal for an experimental mechanism for diagnosis within a knowledge rich supervisory system is explored.

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Bui, Van, Tung Lam Pham, Huy Nguyen, and Yeong Min Jang. "Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals." Applied Sciences 11, no. 5 (March 1, 2021): 2166. http://dx.doi.org/10.3390/app11052166.

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In the last decade, predictive maintenance has attracted a lot of attention in industrial factories because of its wide use of the Internet of Things and artificial intelligence algorithms for data management. However, in the early phases where the abnormal and faulty machines rarely appeared in factories, there were limited sets of machine fault samples. With limited fault samples, it is difficult to perform a training process for fault classification due to the imbalance of input data. Therefore, data augmentation was required to increase the accuracy of the learning model. However, there were limited methods to generate and evaluate the data applied for data analysis. In this paper, we introduce a method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset. The enhanced data set could increase the accuracy of the machine fault detection model in the training process. We also performed fault detection using a variety of preprocessing approaches and classified the models to evaluate the similarities between the generated data and authentic data. The generated fault data has high similarity with the original data and it significantly improves the accuracy of the model. The accuracy of fault machine detection reaches 99.41% with 20% original fault machine data set and 93.1% with 0% original fault machine data set (only use generate data only). Based on this, we concluded that the generated data could be used to mix with original data and improve the model performance.

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Chen, Yifan, Genbao Zhang, and Yan Ran. "Risk Analysis of Coupling Fault Propagation Based on Meta-Action for Computerized Numerical Control (CNC) Machine Tool." Complexity 2019 (July 25, 2019): 1–11. http://dx.doi.org/10.1155/2019/3237254.

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A comprehensive fault analysis of CNC machine tool is conducive to improving its reliability. Due to the highly complex structure of CNC machine tool, there are different degrees of coupling relationship between faults. However, the traditional fault analysis methods (FMEA, FTA, etc.) for CNC machine tool do not solve this problem perfectly. Therefore, we propose a coupling fault propagation model based on meta-action. First, in order to simplify the structural complexity of CNC machine tool, the “Function-Motion-Action (FMA)” decomposition structure is used to decompose the product function into simple meta-action, and the numerical matrix is used to quantify the coupling relationship between the meta-actions. Then, based on the fault transfer characteristics of meta-action, the fault propagation model is established, and the global risk effect (GRE) is combined to realize the comprehensive evaluation of the risk criticality of meta-actions. Finally, the rationality and validity of the method are verified by the case analysis of the automatic pallet changer (APC) of computerized numerical control (CNC) machining center.

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Hwang, G. H., and W. Z. Shen. "Fault analysis and automatic test pattern generation for break faults in programmable logic arrays." IEE Proceedings - Circuits, Devices and Systems 143, no. 3 (1996): 157. http://dx.doi.org/10.1049/ip-cds:19960267.

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Kabilan, R., and G. Selvakumar. "Automatic Speed Control of Motor via WAD Technique for Prevention of Faults in Motor." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 2 (June 1, 2018): 519. http://dx.doi.org/10.11591/ijpeds.v9.i2.pp519-526.

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Many technologies are introduced in monitoring the fault occurrence in the electric motors used in industrial applications. Sound Accusation, current signature analysis, and vibration based motor fault detection systems are widely used in present years. From all these methods analyzing the motor vibration pattern produces more accuracy in finding an occurrence of different faults in the electric motor. The frequency of vibration generated by the MEMS vibration sensor differs for rotor, stator and bearing faults. The signal generated is analyzed using three important techniques namely wavelet analysis, Dyadic Transformation, and Adaptive Neuro-Fuzzy Inference System(WAD Technique). Hardware with ARM microcontroller and ADXL MEMS vibration sensor was used to perform signal acquisition, and the generated signal is processed using the MATLAB software, and the speed of the motor is controlled based on the processed result. The performance of the system with all three algorithms was recorded, and the efficiency of the system is compared.

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Zou, Tao, Xian Lin Zeng, and Jian Hua Peng. "Research on the Airborne Pulse Doppler Radar Jamming System Tester." Applied Mechanics and Materials 654 (October 2014): 250–53. http://dx.doi.org/10.4028/www.scientific.net/amm.654.250.

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This paper introduces the design of an airborne pulse Doppler radar jamming system tester. Based on a detailed analysis of the functional structure of the radar-jamming system, we summarize the important failure mode. To achieve fault location and performance testing, we have developed an automatic detector for the radar-jamming system, the device uses a mode of hardware platform and software platform, and it can reach the purpose of fault location by a method of encouraging input and measuring the output of each of the radar-jamming system failure mode individually. The automatic test instrument has the features of simple operation, high degree of automation, real-time, scalable, and actual use of running proves it to reach the required design specifications, the test with good results.

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Joentgen, A., L. Mikenina, R. Weber, A. Zeugner, and H. J. Zimmermann. "Automatic fault detection in gearboxes by dynamic fuzzy data analysis." Fuzzy Sets and Systems 105, no. 1 (July 1999): 123–32. http://dx.doi.org/10.1016/s0165-0114(98)00442-4.

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Di, Haibin, and Ghassan AlRegib. "Semi‐automatic fault/fracture interpretation based on seismic geometry analysis." Geophysical Prospecting 67, no. 5 (March 13, 2019): 1379–91. http://dx.doi.org/10.1111/1365-2478.12769.

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Zhang, Ying, and Anchen Wang. "Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition." Shock and Vibration 2020 (May 22, 2020): 1–19. http://dx.doi.org/10.1155/2020/6216903.

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This paper proposes a novel method to improve the variational mode decomposition (VMD) method and to automatically acquire the sensitive intrinsic mode function (IMF). First, since fault signals are impulsive and periodic, a weighted autocorrelative function maximum (AFM) indicator is constructed based on the Gini index and autocorrelation function to serve as the optimization objective function. The mode number K and the penalty parameter α of VMD are automatically obtained through an optimal parameter searching process underpinned by the improved particle swarm optimization (PSO) algorithm with a variety of inertia weights. This improvement solves one of the major drawbacks of the conventional VMD method, that is, the need to manually set parameters. Then, an optimal IMF automatic selecting process is performed for single-failure faults and compound faults, according to the principles of the maximum weighted AFM indicator and maximum spectrum peak ratio (SPR), respectively. The sensitive IMFs are then subjected to an envelope demodulation analysis to obtain the fault characteristic frequency. The results of simulations and experiments show that the proposed method can effectively identify fault characteristics early, especially compound faults, demonstrating great potential for real-world applications.

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Zhang, Yi, Yong Lv, and Mao Ge. "A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy." Entropy 23, no. 2 (February 5, 2021): 191. http://dx.doi.org/10.3390/e23020191.

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The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.

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Luo, Ke, and Yingying Jiao. "Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm." PLOS ONE 16, no. 3 (March 24, 2021): e0248515. http://dx.doi.org/10.1371/journal.pone.0248515.

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The purposes are to meet the individual needs of leather production, improve the efficiency of leather cutting, and increase the product’s competitiveness. According to the existing problems in current leather cutting systems, a Fault Diagnosis (FD) method combining Convolutional Neural Network (CNN) and the Support Vector Machine (SVM) of Gray Wolf Optimizer (GWO) is proposed. This method first converts the original signal into a scale spectrogram and then selects the pre-trained CNN model, AlexNet, to extract the signal scale spectrogram’s features. Next, the Principal Component Analysis (PCA) reduces the obtained feature’s dimensionality. Finally, the normalized data are input into GWO’s SVM classifier to diagnose the bearing’s faults. Results demonstrate that the proposed model has higher cutting accuracy than the latest fault detection models. After model optimization, when c is 25 and g is 0.2, the model accuracy can reach 99.24%, an increase of 66.96% compared with traditional fault detection models. The research results can provide ideas and practical references for improving leather cutting enterprises’ process flow.

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Tang, Ran, Shuren Wang, Biao Xiao, and Heng Wang. "Troubleshooting and Improvement of Mechanical Overspeed Shutdown of Diesel Generator." Journal of Physics: Conference Series 2187, no. 1 (February 1, 2022): 012036. http://dx.doi.org/10.1088/1742-6596/2187/1/012036.

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Abstract This paper introduced the components and functions of control components of a ship automatic power station, with an analysis of the control principle of such station. The fault treatment method and troubleshooting process are elaborated based on mechanical overspeed automatic shutdown fault of the generator. Finally, in view of the problem of 4K6 relay, appropriate improvement measures are proposed.

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PENG, MINFANG, JIAJIA WANG, CHI K. TSE, and MEIE SHEN. "COMPLEX NETWORK APPLICATION IN FAULT DIAGNOSIS OF ANALOG CIRCUITS." International Journal of Bifurcation and Chaos 21, no. 05 (May 2011): 1323–30. http://dx.doi.org/10.1142/s0218127411029185.

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Fault diagnosis has played an important role in the identification of fault mechanisms and the subsequent successful isolation of faults in electronic circuits. In this paper, we propose a novel procedure for fault diagnosis in analog circuits. We first generate a set of fault patterns from fault simulation, and our main task is to develop a practical description of the way in which these fault patterns interact. Our approach is based on the construction of a complex network that describes the inter-dependence of the various fault patterns. Analysis of this complex network shows that the degree distribution is scalefree-like and the connectivity is small-world. We henceforth identify a small number of fault patterns that are most highly connected (of highest degrees) with other fault patterns. Furthermore, we study the connection between this network of fault patterns and the original circuit, the purpose being to relate the information of the high-degree fault patterns with the physical circuit topology, thus allowing the physical fault locations and circuit elements to be identified. Our proposed approach will find applications in automatic fault diagnosis of large-scale electronic circuits.

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Journal articles on the topic 'Automatic fault analysis'

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