2020 Vol.3 Jun. No.3

• Title: Research on accurate feature extraction algorithm for bearing fault signal of operating table
• Name: Qi Jun
• Company: Mechatronics and automatic chemistry department,Guangzhou Huali
• Abstract:

  Traditional fault signal feature extraction algorithms such as autocorrelation analysis algorithm, morphological gradient algorithm and other algorithms have the disadvantage of low accuracy. Therefore, a fault signal feature extraction algorithm based on wavelet frequency shift algorithm and minimum entropy algorithm is designed. Based on the noise removal algorithm of mechanical equipment based on wavelet frequency shift design and the mechanical fault identification algorithm based on minimum entropy algorithm, the two algorithms are integrated to generate the feature extraction algorithm of mechanical rolling bearing fault signal. In this way, the feature of fault signal is extracted, and an example is given. The experimental results of simulation and application environment design show that, compared with the traditional design, Compared with the fault signal feature extraction algorithm, the proposed algorithm can improve the accuracy of the analysis results by about 4% when using the same data.

• Keyword: Keywords mechanical rolling bearing;fault signal; extracting signal feature; accuracy;
• DOI: 10.12250/jpciams2020030102
• Citation form: Qi Jun.Research on accurate feature extraction algorithm for bearing fault signal of operating table[J]. Computer Informatization and Mechanical System, 2020, vol. 3, pp. 90-94.

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