Study on Knowledge -based Intelligent Fault Diagnosis of Hydraulic System

A general framework of hydraulic fault diagnosis system was studied. It consisted of equipment knowledge bases, databases, fusion reasoning, knowledge acquisition and so on. The tree-structure model of the fault knowledge was established based on fault hierarchy and logicality. Fault nodes knowledge was encapsulated by object-oriented technique. Complete knowledge bases were made including fault bases and diagnosis bases. It could describe the fault positions, system structure, cause-symptom relationships, diagnosis principles and other knowledge. The results show that the methods are effective.

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Fault Diagnosis Method for Marine Hydraulic System

ICLEM 2012 ◽

10.1061/9780784412602.0104 ◽

2012 ◽

Author(s):

Changwei He ◽

Dawei Liu

Keyword(s):

Fault Diagnosis ◽

Hydraulic System ◽

Diagnosis Method

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Analytical and knowledge-based redundancy for fault diagnosis in process plants

AIChE Journal ◽

10.1002/aic.690390107 ◽

1993 ◽

Vol 39 (1) ◽

pp. 42-56 ◽

Cited By ~ 26

Author(s):

Zohreh Fathi ◽

W. Fred Ramirez ◽

Jozef Korbicz

Keyword(s):

Fault Diagnosis ◽

Knowledge Based ◽

Process Plants

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Construction of a deep sparse filtering network for rotating machinery fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211014852 ◽

2021 ◽

pp. 095440702110148

Author(s):

Chun Cheng ◽

Wei Zou ◽

Weiping Wang ◽

Michael Pecht

Keyword(s):

Fault Diagnosis ◽

Back Propagation ◽

Feature Learning ◽

Back Propagation Neural Network ◽

Rotating Machinery ◽

Fine Tuning ◽

Local Optimum ◽

Back Propagation Algorithm ◽

Intelligent Fault Diagnosis ◽

Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

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