Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group

Abstract

Bearings are a critical component of rotating machines; when they fail, critical equipment becomes unavailable, damage may occur beyond the bearing itself, and safety concerns arise. Determining that a bearing structure is compromised before catastrophic failure permits the protection of plant, people, and productivity. When bearings malfunction, the features of single and multiple faults are masked and accompanied by noise and other signal degrading artifacts affecting the signals from the vibrational sensors. In these circumstances, detection and diagnosis of multistate bearing faults is difficult. To overcome these challenges, an improved convolutional sparse coding (ICSC) model, based on a priori periodic filter groups (PPFG), is proposed to respond to the multistate fault problems of bearings. A Laplace wavelet is constructed with one-sided decay related to the vibration pattern of the signal. The best-matched wavelet is optimally determined by correlation analysis of the signal frequency domain parameters and the time domain damping parameters. The best-matched wavelet and the kurtosis criterion are used to construct a PPFG based on the theoretical period of the fault. The ICSC based on the PPFG obtains mapping coefficients characterizing different vibrational features of the signal. The envelope spectrum analysis of the various mapping coefficients identifies and confirms the fault-revealing components in the multistate signal. The ICSC results have a relatively good sparse time domain, and the fault-identifying features in the envelope spectrum are enhanced. Multiple faults can be easily identified. The effectiveness and robustness of the PPFG/ICSC are demonstrated through a complete experimental analysis of simulated, single-fault, and multifault signals, as well as a comparative analysis of the previous methods – Fast SK, CBPDN, and VMD-ICA – which verifies that the PPFG/ICSC is more robust, accurate, and efficient than the previous methods.