Improved Optimized Minimum Generalized Lp/Lq Deconvolution and Application to Bearing Fault Detection

Locating the fault-induced cyclic impulses from corrupted vibration signals is a key step in detecting bearing fault characteristics. Recently, a novel deconvolution technique named the optimized minimum generalized Lp/Lq deconvolution (OMGD) was proposed and has been validated as a useful technique to highlight the periodic impulses related to bearing faults. However, the performance of the OMGD is associated with the appropriate selection of prior parameters, such as the filter length. In addition, the OMGD faces edge effect issues, leading to a shorter duration of the enhanced signal when compared to the measured signal. To overcome the shortcomings of the OMGD, this study proposes an improved version, termed the IOMGD. The enhanced technique employs an advanced sparrow search algorithm to automatically ascertain the filter length, doing away with the need for a predetermined fixed value. To solve the problem of the edge effect, a data extension technique based on the autoregressive model (AR-DET) is proposed to adaptively recover the length of the filtered signal to match that of the raw signal based on the properties observed at the filtered signal. The IOMGD’s superiority over the original OMGD has been substantiated by its performance on various real-world bearing fault datasets. Furthermore, a comparative analysis is performed between the IOMGD and other commonly used bearing fault diagnosis methods, revealing the superiority of the IOMGD.