Compound faults feature detection of frequency correlation kernel kurtosis

Yishi Tong; Yueting Yang; Liu Yang; Yuhu Zuo

doi:10.1117/12.2652447

10 November 2022 Compound faults feature detection of frequency correlation kernel kurtosis

Yishi Tong, Yueting Yang, Liu Yang, Yuhu Zuo

Proceedings Volume 12331, International Conference on Mechanisms and Robotics (ICMAR 2022); 1233131 (2022) https://doi.org/10.1117/12.2652447
Event: International Conference on Mechanisms and Robotics (ICMAR 2022), 2022, Zhuhai, China

Abstract

Axle box bearings for locomotives play a significant role in guaranteeing the service performance of the locomotives. This paper has studied how to extract the weak feature of compound fault under strong interference excitations and proposed a kernel correlated kurtosis of square envelope spectrum. Firstly, simulated compound fault signals were applied to analyze important issue of its demodulation; Three signal processing tools are compared to evaluate their detection performance. Secondly, frequency domain correlated kernel was used to quantitatively compute the amplitude value of signal squared envelope spectrum, settle the failure that conventional time-frequency kurtosis fail identify signals of a strong pulse interference fault; namely, with frequency-domain correlated kernel values, Kurtogram is generated. Which can identify the optimum frequency range and decomposed the single fault feature adaptively. Finally, the proposed method was proved effective and applicable in contrast with experimental results.

Citation Download Citation

Yishi Tong, Yueting Yang, Liu Yang, and Yuhu Zuo "Compound faults feature detection of frequency correlation kernel kurtosis", Proc. SPIE 12331, International Conference on Mechanisms and Robotics (ICMAR 2022), 1233131 (10 November 2022); https://doi.org/10.1117/12.2652447

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