RESEARCH PAPER
Fault Diagnosis Model of Hydraulic Motor Based on Fuzzy Neural Network
1
Energy and Power Engineering, Lanzhou University of Technology, China
Submission date: 2024-06-19
Final revision date: 2024-09-02
Acceptance date: 2024-11-03
Online publication date: 2024-11-11
Publication date: 2024-11-11
Corresponding author
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):195532
HIGHLIGHTS
- Fuzzy neural network is used to predict hydraulic motor faults.
- The feature vector is output in the global mean pooling layer.
- The dynamic cluster graph is obtained by fuzzy clustering.
KEYWORDS
TOPICS
ABSTRACT
When the hydraulic motor fault occurs,it is not easy to be detected,and the leakage degree will gradually increase.In order toavoid bigger accidents causedby thehydraulic motor fault,the accident isexcluded in theembryonic stage,and the hydraulic motor fault prediction method based onfuzzy neural network isused topredict thehydraulic motor fault.The feature vector is output inthe global meanpooling layer,andthe feature vectormatrix between thehealth state feature vector library and the samples to be measured is constructed.The dynamic cluster graph isobtained by fuzzy clustering,so as to realize the fault diagnosis of thehydraulic motor.The results show that the accuracy of training set,verification set andtest set ishigher than99.8%.The accuracy of diagnosis classification is99.00%,which is better than othercomparison models.In this study,the number of training samples can be appropriately increased ordecreased according to thecurve complexity of the detection target,so as to improve thefeature extraction capability of the convolutional layer andincrease the classification accuracy.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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