Generalized broadband mode decomposition method and its application in fault diagnosis of variable speed spherical roller bearing. (28th February 2023)

• Record Type: Journal Article
• Authors: Geng, Hongyan; Peng, Yanfeng; Ye, Long; Guo, Yong
• Is Part Of: Measurement. Volume 208(2023)
• Publication Date: 2023-02-28
• Subjects: Spherical roller bearing -- Variable speed -- Generalized broadband mode decomposition -- Envelope order spectrum -- Fault diagnosis; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2023.112450
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Generalized broadband mode decomposition (GBMD) method is proposed for decomposing signals of rolling bearing with variable speed disturbed by strong noise. An associative dictionary library consisting of typical broadband signals and narrowband signals is constructed. The decomposition capabilities of EEMD, BMD, and GBMD methods are compared in detail, including the decomposition of multi-component mixed signal, different SNRs and slopes of frequency, and different amplitude ratios and slope ratios of frequency. Compared with EEMD and BMD, GBMD can accurately extract fault features and combine with envelope order spectrum to realize fault diagnosis of spherical roller bearing with variable speed. Abstract: Fault signals of roller bearing with variable speed often present non-stationary characteristics which are difficult to be accurately extracted by existing analytical methods. The previously proposed broadband mode decomposition (BMD) method is to search in an associative dictionary library containing all signals to extract stationary signals. However, when applied to the variable speed signals interfered by strong noise, BMD will have a large deviation. Therefore, considering the shortcomings of BMD, a generalized broadband mode decomposition (GBMD) method is proposed by combining generalized demodulation and BMD. Firstly, the vibration signal is decomposed by GBMD. Secondly, the first three components are resampled in the angular domain. Finally, the envelope order spectrum of the angular signal is analyzed. The analysis shows that, compared with EEMD and BMD, GBMD not only has strong decomposition capability, but also can effectively extract fault features to achieve fault diagnosis of spherical roller bearing with variable speed.