Engine Fault Diagnosis Based on Support Vector Machine and Time-Frequency Domain Analysis

Research Area: Volume 13, Issue 5, Sept. 2024 Year: 2024
Type of Publication: Article Keywords: Engine, Time-Frequency Analysis, Support Vector Machine, Fault Diagnosis
Authors:
  • Kunming Gao
Journal: IJEIR Volume: 13
Number: 5 Pages: 56-64
Month: September
ISSN: 2277-5668
Abstract:
In order to effectively improve the accuracy of the engine connecting rod fault diagnosis algorithm and ensure more reliable operation of the engine, a fault diagnosis algorithm for engine connecting rods is proposed based on engine related vibration data and combined with support vector machines. Firstly, the vibration data is preprocessed and analyzed in time domain, frequency domain and time-frequency domain; Then, the kurtosis value in time domain, the energy value in frequency domain, the singular value of wavelet packet coefficient matrix and the energy ratio eigenvalue of each frequency band of wavelet packet are extracted, and the eigenmatrix is constructed; Secondly, the eigenvalue matrix is introduced into the linear vector machine for training, and the fault diagnosis model is obtained. Finally, the validity of the fault diagnosis model is verified by the measured sample data on the bench. The results show that the fault diagnosis accuracy of the algorithm is 99.5%, which meets the requirements of fault diagnosis, and the algorithm can accurately identify engine connecting rod faults.

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