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.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Chen, Y. (2019). Research on Accurate Extraction Algorithm for Fault Signal Characteristics of Mechanical Rolling Bearings. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_29
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DOI: https://doi.org/10.1007/978-3-030-19086-6_29
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