Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Learning polynomial networks for classification of clinical electroencephalograms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Sequential Fuzzy Diagnosis for Condition Monitoring of Rolling Bearing Based on Neural Network
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
WSEAS TRANSACTIONS on SYSTEMS
Condition diagnosis method based on statistic features and information divergence
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Computers and Industrial Engineering
Fault diagnosis method of machinery based on fisher's linear discriminant and possibility theory
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Automatic bearing fault diagnosis based on one-class ν-SVM
Computers and Industrial Engineering
Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks
International Journal of Software Science and Computational Intelligence
Computers and Industrial Engineering
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Fault diagnosis and condition surveillance of rotating machinery in a plant is very important for guaranteeing production efficiency and plant safety. In a large plant, with an enormous number of rotating machines, condition surveillance and fault diagnosis for all rotating machines is not only time consuming and labor intensive, but the accuracy of condition judgment cannot be ensured. These difficulties may cause serious machine accidents and consequently great production losses. In order to improve the efficiency of condition surveillance and detect faults at an early stage, this paper proposes a method of condition surveillance and fault discrimination for rotating plant machinery using non-dimensional symptom parameters in a time domain and ''Partially-linearized Neural Network'' (PLNN), from which the state of a rotating machine can be discriminated automatically. The verification results of precise diagnosis for rolling bearings show that the PLNN can effectively distinguish bearing faults. The verification results for condition surveillance of rotating machinery in a real plant show that the PLNN correctly judges the machine state of the inspected rotating machine as normal or abnormal.