48 / 2025-05-13 16:14:13

Adaptive periodic detection blind deconvolution

Fault Diagnosis,Blind Deconvolution,fault characteristic frequency extraction,Bearing Diagnosis

Due to the over-reliance on prior knowledge and ignoring the influence of filter length on the deconvolution results, the traditional blind deconvolution method has many difficulties in extracting the weak fault characteristics of the bearing and estimating the signal influence period, resulting in poor robustness. Based on the powerful influence feature localization ability of Envelope Product Spectrum (EHPS) and the excellent dynamic search performance of pruning strategy, an adaptive fault cycle detection blind deconvolution method for intelligent bearing fault diagnosis in rail transit was proposed. The envelope product spectrum provides the optimization framework for achieving maximum second-order cyclostationary blind deconvolution (CYCBD) without a priori information, and the fault harmonic energy ratio (PFHE) is constructed by combining the distribution characteristics of fault-sensitive components over the predetermined bandwidth, which is expressed as the intensity of the fault influence characteristics. Subsequently, the pruning strategy was combined with the third-way method to find the optimal filter length, so as to realize the adaptive selection of multi-scale filtering parameters. Simulation and experimental data confirm that the presented algorithm has better robustness and superiority than the other three kinds of blind convolution. In addition, in contrast to conventional filter length sweeping approaches, the presented algorithm not only achieves accurate condition assessment, but also significantly enhances computational search performance.