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Intelligent Fault Diagnosis with Deep Architecture
2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2020The status judgment of converter valve equipment is an important part of ultra high voltage maintenance. However, accurate judgment of possible failures remains challenging. This paper proposes a novel multi-region deep architecture to improve abnormal judgement by considering long-range context information in increasingly finer spatial regions ...
Jinrui Gan +5 more
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Computational intelligence based machine fault diagnosis
Proceedings of the IEEE International Conference on Industrial Technology (ICIT'96), 2002Machine fault diagnosis is a well established area where specific techniques are used to determine fault patterns or locations. In recent years, there are many studies about this issue by means of model based approach, probabilistic method, knowledge based approach and neural networks based approach et al.
D.D. Wang +3 more
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Intelligent Fault Diagnosis in Nonlinear Systems
Intelligent Automation & Soft Computing, 2013Fault diagnosis in nonlinear systems is a challenging and very active research area. One of the difficulties to detect and isolate faults in nonlinear systems via observer-based methods is the design of a residual generator. In this work an integrated procedure combining conventional decoupling methods and Fuzzy Takagi-Sugeno observers for fault ...
E. Alcorta-Garcia +2 more
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Intelligent fault diagnosis for robotic systems
11th IEEE International Conference on Control & Automation (ICCA), 2014Robotic systems are widely used in industry. Preventive maintenance of electrical machine systems plays a very important role in the industrial life. This requires monitoring their operations on-line which can detect a fault as it occurs and diagnosing the malfunction of a faulty component. In this paper, we present a fault diagnosis method for robotic
Mingbo Xiao +2 more
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Intelligent fault diagnosis for analog circuits
Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, 2011This paper describes the design of fault diagnosis system based on neural networks and expert system for analog circuit. This system avoids wrong output from neural networks, the other hand reduces the missed diagnosis from expert system, and simulates for analog circuit with Matlab.
Rui Chen, Zhenchao Zhou
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Intelligent fault diagnosis of synchronous generators
Expert Systems with Applications, 2016A 3?kVA generator fault model is used to diagnose faults in a 5 kVA generator.The model is trained using 3 kVA generator data and 5 kVA generator (no-fault data).System-dependent dimensions are removed using nuisance attribute projection (NAP).Classification and regression tree (CART) is used as a back-end classifier with NAP.NAP improves the ...
R. Gopinath +4 more
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Study of hybrid intelligent fault diagnosis
2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010), 2010A hybrid intelligent fault diagnosis method is presented for the diversity, uncertainty and complexity of device faults. This method integrates respective advantages of fault tree, fuzzy theory, neural networks and genetic algorithms to form a hybrid approach and is applied to fault diagnosis of fan.
null Guohuan Lou +2 more
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Learning Mechanisms for Intelligent Fault Diagnosis
2006 IEEE International Conference on Systems, Man and Cybernetics, 2006Early diagnosis of plant faults / deviations is a critical factor for optimized and safe plant operation and maintenance. Although smart controllers and diagnosis systems are available and widely used in chemical plants, however, some faults couldn't be detected.
Hossam A. Gabbar +4 more
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Artificial intelligence approaches to fault diagnosis
IEE Colloquium on Condition Monitoring Machinery, External Structures and Health, 1999Fault diagnosis of control engineering systems can be based upon the generation of signals which reflect inconsistencies between the fault-free and faulty system operation-so-called residual signals. This paper outlines some recent approaches to the generation of residual signals using methods of integrating quantitative and qualitative system ...
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