Asymmetric penalty sparse model based cepstrum analysis for bearing fault detections. (August 2020)

• Record Type: Journal Article
• Authors: Liu, Yi; Jiang, Zhansi; Haizhou, Huang; Xiang, Jiawei
• Is Part Of: Applied acoustics. Volume 165(2020)
• Publication Date: 2020-08
• Subjects: Asymmetric penalty sparse model -- Cepstrum analysis -- Stationary and non-stationary conditions -- Bearings -- Fault detection; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2020.107288
• Languages: English
• ISSNs: 0003-682X

Abstract

Graphical abstract: Case 1. The scheme of the APSM enhanced cepstrum analysis to detect faults under constant speed condition. Case 2. The scheme of the APSM enhanced cepstrum analysis to detect faults under variable speed conditions. Highlights: Asymmetric penalty sparse model (APSM) enhanced cepstrum analysis is proposed for fault detection. APSM and cepstrum analysis are applied to purify vibration signals and effectiveness extract periodic impulses, respectively. The superiority is verified by numerical and experiment investigations both in stationary and non-stationary conditions. For non-stationary condition, multi-synchrosqueezing transform (MSST) is employed for non-stationary conditions to estimate the instantaneous frequency (IF) to achieve the tacholess measurement. Abstract: Signal processing approaches of rotating machine are well-established and wildly used in machine condition monitoring. Cepstrum analysis has the ability to transform related components from convolution form to addition form and suitable for the extraction of the periodic impulse components. However, the fault features are often localized in heavy background noises caused by the operation condition, which limit the application of cepstrum analysis. In this paper, a method is proposed by introducing the asymmetric penalty sparse model (APSM) to improve the effectiveness of cepstrum. The core idea of APSM is a sparse optimization formulation solved by the alternating direction method to achieve sparse feature extraction without any prior knowledge. The detection of bearing faults both in stationary and non-stationary conditions are given by using simulation and experimental signals. The performance of the proposed method is verified and the superiority is addressed by comparing with cesptrum analysis.