Compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum and AO-MOMDEA

• Published in: Measurement science & technology 2023-09, Vol.34 (9), p.95012
• Main Authors: Wang, Huibin, Yan, Changfeng, Wang, Zonggang, Liu, Bo, Li, Shengqiang, Wu, Lixiao
• Format: Article
• Language: English
• ISSN: 0957-0233; 1361-6501
• DOI: 10.1088/1361-6501/acd710

In compound rolling bearing faults, different types of fault signals are coupled and cross-affected, which makes it difficult to separate and extract weak fault features, and making it easy to miss diagnoses or misdiagnose. Aiming at solving this problem, a compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum (MKS) and the aquila optimizer multipoint optimal minimum entropy deconvolution adjusted (AO-MOMEDA) method is proposed. Variational modal decomposition is used to reduce the noise of the original vibration signal and improve the recognition of the MKS so as to predict the fault type and preset the search range of the initial period value. A new fault feature strength index with relative amplitude difference is proposed and, combined with the kurtosis of the envelope spectrum, a multi-objective optimization function is constructed. Through the excellent optimization characteristics of the aquila optimizer, the optimal initial period value and filter length are automatically obtained. The optimized MOMEDA method is used for optimal deconvolution of different types of fault signals. Envelope demodulation of the deconvolution signal is carried out to obtain the 1.5-dimensional spectrum of the envelope signal. The types of rolling bearing composite faults are identified according to the fault features in the 1.5-dimensional spectrum. The feasibility of the proposed method is verified through measurements of the signals of rolling bearings with three different types of compound faults. The experimental results show that the proposed method can overcome the influence of strong fault signals and improve the accuracy of compound fault identification. Our results have application value in engineering practice.