Relevant bibliographies by topics / Fault Diagnosis Toolbox

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Fault Diagnosis Toolbox'

Author: Grafiati
Published: 25 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Fault Diagnosis Toolbox"

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Tang, Jia Li, Chen Rong Huang, and Jian Min Zuo. "Gear Fault Diagnosis with Support Vector Machine." Advanced Materials Research 455-456 (January 2012): 1169–74. http://dx.doi.org/10.4028/www.scientific.net/amr.455-456.1169.

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Because of the complexity of gear working condition, there are non-linear relationship between characteristic parameters and fault types. This paper proposes to apply the Support Vector Machine to set up the nonlinear mapping to solve the difficulties of gear fault diagnosis. Taking a certain gearbox fault signal acquisition experimental system for instance, Matlab software and its neural network toolbox are used to model and simulate. The simulation result shows the founded model has preferable learning and generalization capabilities, which performs effectively in the common gear fault diagnosis and it can identify various types of faults stably and accurately.
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Clever, Sebastian, Marco Muenchhof, and Daniel Mueller. "A Fault Diagnosis Toolbox Applying Classification and Inference Method." IFAC Proceedings Volumes 42, no. 8 (2009): 486–91. http://dx.doi.org/10.3182/20090630-4-es-2003.00081.

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Liu, Xi Mei, Xiao Hui Yao, Qian Zhao, and Hong Mi Guo. "Application of RBF Neural Network in Fault Diagnosis for Transmission Gear." Advanced Materials Research 433-440 (January 2012): 7563–68. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.7563.

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A method for transmission gearbox fault diagnosis is put forward in this paper by using radial basis function neural network (RBF network). A RBF neural network is created to simulate the gearbox fault diagnosis using Matlab neural network toolbox. Compared with BP neural network, RBF network is superior to the former in accuracy and speed according to the simulate results. This method is accurate and credible in gear fault diagnosis, and it has a broad application prospect in mechanical fault diagnosis.
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Gu, Jun. "Study on on-Line Fault Diagnosis of Torque and Position Sensor of EPS Based on Artificial Neural Networks." Advanced Materials Research 490-495 (March 2012): 638–42. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.638.

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The strategy of the On-line Fault Diagnosis of the torque and position sensor of the EPS system based on the artificial neural networks (ANN) is advanced in this paper. The strategy is modeled, simulated and analyzed in ANN Toolbox of Mat lab. The results show that the strategy is effective and it can be applied in the development of EPS fault diagnosis.
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Danish Anis, Mohamad. "A Defect Diagnosis in Bearings of a Centrifugal Pump using Vibration Analysis." Global Journal of Enterprise Information System 9, no. 1 (May 5, 2017): 51. http://dx.doi.org/10.18311/gjeis/2017/15865.

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This paper presents an overview of defect diagnosis in bearings of a centrifugal pump. The data obtained using vibration-based condition monitoring (VCM) technique was recorded at regular intervals. The analysis provided using conventional methods would then be used for pump fault prognosis and trend pump conditions. Having studied the conventional method of analysing results off-line, the research uses a VCM system to predict bearing faults on-line. Several techniques for pattern recognition were considered, including Feed Forward type Neural Network (FF-NN) and Recurrent Neural Networks (RNN). The author decided to adopt the Artificial Neural Network (ANNs) to propose a solution and classify bearing faults. Since bearing faults don’t begin to appear before prolonged pump operations, the faults on bearings were simulated using a test-rig pump where an electrical discharge machine (EDM) would generate pit marks on bearings and the vibration signals thus collected be fed into the neural networks. An easy method of designing neural network models is by using the MATLAB Neural Network Toolbox. To carry out the analysis, only MATLAB models that are specifically functional to vibration signals are chosen for pump bearing fault diagnosis.
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Zuo, Gui Lan, Shang Ding Lai, and Yue Cheng. "Study on the Fault Diagnosis of Gear Pump Based on PNN Neural Network." Advanced Materials Research 1044-1045 (October 2014): 873–76. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.873.

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The principle of neural network’s PNN algorithm was introduced, Combining with the structure feature and work principle of the hydraulic pump, a fault diagnosis system based on PNN neural network was established. The feasibility of the system was proved through the identification, emulation and experimentation of hydraulic system’s fault patterns. The PNN control model was simulated using Matlab/Simulink toolbox. This model analyzed and studied the PNN network predictive diagnostic rate. Under different sample size and SPREAD, the simulation’s results show that this method has favorable identified capability of fault mode and favorable applicability to the hydraulic pump.
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Yu, Kuo-Nan, Her-Terng Yau, and Jian-Yu Li. "Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/280520.

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At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT) for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.
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Herve, Samba Aime, Yeremou Tamtsia Aurelien, Hermine Som Idellette Judith, and Nneme Nneme Leandre. "Networked Iterative Learning Fault Diagnosis Algorithm for Systems with Sensor Random Packet Losses, Time-varying Delays, Limited Communication and Actuator Failure : Application to the Hydroturbine Governor System." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (May 20, 2021): 244–52. http://dx.doi.org/10.37394/23203.2021.16.20.

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An iterative learning fault diagnosis (ILFD) algorithm for networked control systems (NCSs) subject to random packet losses, time-varying delays, limited communication and actuator failure is proposed in this paper. Firstly, in order to evaluate the effect of fault on system between every iteration, the information of state error and information of fault tracking estimator from the preceding iteration are used to improve the fault estimation achievement in the actual iteration. The state variable, the Bernoulli process of random packet losses, network communication delay, limited communication and actuator failure are introduced to establish an extended statespace model of the system. Secondly combining Lyapunov stability theory for linear repetitive processes and linear matrix inequality (LMI) technique, new sufficient condition for the existence of an iterative learning fault diagnosis is established. Finally, the feasibility and effectiveness of the proposed design method is illustrated on a dynamic hydroturbine governing system model based on Matlab/Simulink and TrueTime toolbox
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Lv, Mingzhu, Xiaoming Su, and Shixun Liu. "Application of SVM Optimized by IPSO in Rolling Bearing Fault Diagnosis." MATEC Web of Conferences 227 (2018): 02007. http://dx.doi.org/10.1051/matecconf/201822702007.

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Aiming at the problem that the classification effect of support vector machine (SVM) is not satisfactory due to improper selection of penalty factor C and kernel parameter g, this paper proposes a new modified classifier that uses the improved particle swarm optimization (IPSO) to optimize the parameter of SVM (IPSO-SVM) by introducing the dynamic inertia weight, global neighborhood search, population shrinkage factor and particle mutation probability. The classification result is tested by Common data sets named BreastTissue、 Heart and Wine from the Libsvm toolbox, the results show that IPSO-SVM classifier is obviously superior to SVM and PSO-SVM classifier in terms of prediction accuracy and classification time. Then it is applied to the fault diagnosis in two classification problems and multiple classification problems of rolling bearings. The simulation results show that the IPSO-SVM classifier has stronger global convergence ability and faster convergence speed, and the ideal classification results can be obtained. Finally, the IPSO-SVM classifier is used to diagnose the fault of the actual bearing. The results show that the classifier has a better classification stability and is worthy of further promotion in engineering field.
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Ewert, Pawel, Czeslaw T. Kowalski, and Teresa Orlowska-Kowalska. "Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach." Electronics 9, no. 9 (August 19, 2020): 1334. http://dx.doi.org/10.3390/electronics9091334.

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In this article, a low-cost computer system for the monitoring and diagnosis of the condition of the induction motor (IM) rolling bearings is demonstrated and tested. The system allows the on-line monitoring of the IM bearings and subsequent fault diagnostics based on analysis of the vibration measurement data. The evaluation of the bearing condition is made by a suitably trained neural network (NN), on the basis of the spectral and envelope analysis of the mechanical vibrations. The system was developed in the LabVIEW environment in such a way that it could be run on any PC. The functionality of the application has been tested on a real object. The study was conducted on a low-power IM equipped with a set of specially prepared bearings so as to model the different damages. In the designed computer system, a selected NN for detecting and identifying the defects of individual components of the induction motor’s bearings was implemented. The training data for NNs were obtained from real experiments. The magnitudes of the characteristic harmonics, obtained from the spectral analysis and the envelope analysis, were used for training and testing the developed neural detectors based on Matlab toolbox. The experimental test results of the developed monitoring and diagnosis system are presented in the article. The evaluation of the system’s ability to detect and identify the defects of individual components of bearings, such as the rolling element, and outer race and inner race, was made. It was also shown that the developed NN-based detectors are insensitive to other motor faults, such as short-circuits of the stator winding, broken rotor bars or motor misalignment.
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Dissertations / Theses on the topic "Fault Diagnosis Toolbox"

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Lannerhed, Petter. "Structural Diagnosis Implementation of Dymola Models using Matlab Fault Diagnosis Toolbox." Thesis, Linköpings universitet, Fordonssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138753.

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Models are of great interest in many fields of engineering as they enable prediction of a systems behaviour, given an initial mode of the system. However, in the field of model-based diagnosis the models are used in a reverse manner, as they are combined with the observations of the systems behaviour in order to estimate the system mode. This thesis describes computation of diagnostic systems based on models implemented in Dymola. Dymola is a program that uses the language Modelica. The Dymola models are translated to Matlab, where an application called Fault Diagnosis Toolbox, FDT is applied. The FDT has functionality for pinpointing minimal overdetermined sets of equations, MSOs, which is developed further in this thesis. It is shown that the implemented algorithm has exponential time complexity with regards to what level the system is overdetermined,also known as the degree of redundancy. The MSOs are used to generate residuals, which are functions that are equal to zero given that the system is fault-free. Residual generation in Dymola is added to the original methods of the FDT andthe results of the Dymola methods are compared to the original FDT methods, when given identical data. Based on these tests it is concluded that adding the Dymola methods to the FDT results in higher accuracy, as well as a new way tocompute optimal observer gain. The FDT methods are applied to 2 models, one model is based on a system ofJAS 39 Gripen; SECS, which stands for Secondary Enviromental Control System. Also, applications are made on a simpler model; a Two Tank System. It is validated that the computational properties of the developed methods in Dymolaand Matlab differs and that it therefore exists benefits of adding the Dymola implementations to the current FDT methods. Furthermore, the investigation of the potential isolability based on the current setup of sensors in SECS shows that full isolability is achievable by adding 2 mass flow sensors, and that the isolability is not limited by causality constraints. One of the found MSOs is solvable in Dymola when given data from a fault-free simulation. However, if the simulation is not fault-free, the same MSO results in a singular equation system. By utilizing MSOs that had no reaction to any modelled faults, certain non-monitored faults is isolated from the monitored ones and therefore the risk of false alarms is reduced. Some residuals are generated as observers, and a new method for constructing observers is found during the thesis by using Lannerheds theorem in combination with Pontryagin’s Minimum Priniple. This method enables evaluation of observer based residuals in Dymola without any selection of a specific operating point, as well as evaluation of observers based on high-index Differential Algebraic Equations, DAEs. The method also results in completely different behaviourof the estimation error compared to the method that is already implemented inthe FDT. For example, one of the new observer-implementations achieves both an estimation error that converges faster towards zero when no faults are implementedin the monitored system, and a sharper reaction to implemented faults.
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Kleman, Björn, and Henrik Lindgren. "Evaluation of model-based fault diagnosis combining physical insights and neural networks applied to an exhaust gas treatment system case study." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176650.

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Fault diagnosis can be used to early detect faults in a technical system, which means that workshop service can be planned before a component is fully degraded. Fault diagnosis helps with avoiding downtime, accidents and can be used to reduce emissions for certain applications. Traditionally, however, diagnosis systems have been designed using ad hoc methods and a lot of system knowledge. Model-based diagnosis is a systematic way of designing diagnosis systems that is modular and offers high performance. A model-based diagnosis system can be designed by making use of mathematical models that are otherwise used for simulation and control applications. A downside of model-based diagnosis is the modeling effort needed when no accurate models are available, which can take a large amount of time. This has motivated the use of data-driven diagnosis. Data-driven methods do not require as much system knowledge and modeling effort though they require large amounts of data and data from faults that can be hard to gather. Hybrid fault diagnosis methods combining models and training data can take advantage of both approaches decreasing the amount of time needed for modeling and does not require data from faults. In this thesis work a combined data-driven and model-based fault diagnosis system has been developed and evaluated for the exhaust treatment system in a heavy-duty diesel engine truck. The diagnosis system combines physical insights and neural networks to detect and isolate faults for the exhaust treatment system. This diagnosis system is compared with another system developed during this thesis using only model-based methods. Experiments have been done by using data from a heavy-duty truck from Scania. The results show the effectiveness of both methods in an industrial setting. It is shown how model-based approaches can be used to improve diagnostic performance. The hybrid method is showed to be an efficient way of developing a diagnosis system. Some downsides are highlighted such as the performance of the system developed using data-driven and model-based methods depending on the quality of the training data. Future work regarding the modularity and transferability of the hybrid method can be done for further evaluation.
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Book chapters on the topic "Fault Diagnosis Toolbox"

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Frisk, Erik, Mattias Krysander, Mattias Nyberg, and Jan Åslund. "A Toolbox for Design of Diagnosis Systems." In Fault Detection, Supervision and Safety of Technical Processes 2006, 657–62. Elsevier, 2007. http://dx.doi.org/10.1016/b978-008044485-7/50111-1.

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Conference papers on the topic "Fault Diagnosis Toolbox"

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Cruz-Manzo, Samuel, Vili Panov, Yu Zhang, Anthony Latimer, and Festus Agbonzikilo. "A Thermodynamic Transient Model for Performance Analysis of a Twin Shaft Industrial Gas Turbine." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64376.

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In this study, a Simulink model based on fundamental thermodynamic principles to predict the dynamic and steady state performance in a twin shaft Industrial Gas Turbine (IGT) has been developed. The components comprising the IGT have been implemented in the modelling architecture using a thermodynamic commercial toolbox (Thermolib, EUtech Scientific Engineering GmbH) and Simulink environment. Measured air pressure and air temperature discharged by compressor allowed the validation of the modelling architecture. The model assisted the development of a computational tool based on Artificial Neural Network (ANN) for compressor fault diagnostics in IGTs. It has been demonstrated that modelling plays an important role to predict and monitor gas turbine system performance at different operating and ambient conditions.
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