Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance

In engineering practice, the bearing fault signal is composed of a series complex multi-component signals containing multiple characteristics information. early stage sprouting and evolution, features are easily disturbed by noise irrelevant signals, eliminating in strong background noise. To overcome influence on signal, this study proposes multi-frequency weak decomposition reconstruction rolling based adaptive cascaded stochastic resonance. First, original passed through Hilbert transform to obtain envelope signal. The high-pass filtered eliminate interference low-frequency components response resonance system. Secondly, system parameters adaptively optimized quantum particle swarm algorithm (QPSO). input (ACSRS) can further enhance characteristics, allowing gradual transfer high-frequency energy characteristic components. Finally, decomposed using variational mode (VMD) method jointly determine location frequencies intrinsic functions (IMF) component loss coefficient correlation achieve signals. Through simulation experimental validation, effectiveness superiority for detection bearings verified. results show that not only achieves optimization gradually removing improving but also reduces number layers VMD, enhances information effectively identifies bearings.