Research on Gearbox Fault Diagnosis Method Based on VMD and Optimized LSTM
Source abstract
Abstract
The reliability of gearboxes is extremely important for the normal operation of mechanical equipment. This paper proposes an optimized long short-term memory (LSTM) neural network fault diagnosis method. Additionally, a feature extraction method is employed, utilizing variational mode decomposition (VMD) and permutation entropy (PE). Firstly, the gear vibration signal is subjected to feature decomposition using VMD. Secondly, PE is calculated as a feature quantity output. Next, it is input into the improved LSTM fault diagnosis model, and the LSTM parameters are iteratively optimized using the chameleon search algorithm (CSA). Finally, the output of the fault diagnosis results is obtained. The experimental results show that the accuracy of the method exceeds 97.8%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Gear and Bearing Dynamics Analysis · Industrial Technology and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130103, China
School of Mechanical and Electrical Engineering, Changchun Institute of Technology, Changchun 130103, China
Funding
Science and Technology Department of Jilin Province · Major Science and Technology Projects of Jilin Province and Changchun City
References (24)
Show source-provided references
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