Bearing Fault Diagnosis via FMD with Parameters Optimized by an Improved Crested Porcupine Optimizer

Feature Mode Decomposition (FMD) can effectively extract bearing fault features even in the case of strong interference noise by means of adaptive finite impulse response filter banks along with correlated kurtosis. Nevertheless, the filter length L and the number of decomposition modes K need to be predefined carefully in a manual way. Otherwise, mismatched parameters could lead to redundant components or even missed detection of fault information. To mitigate the reliance on manual parameter setting, recent studies have introduced optimization algorithms such as the Whale Optimization Algorithm and the Crested Porcupine Optimizer to find the optimal parameters for FMD. However, such methods usually suffer from the dilemma of easily premature convergence in global search and long-time consumption in local fine adjustment, rendering them with difficulty in meeting the requirements of real-time and accurate diagnosis. Therefore, this paper proposes an improved Crested Porcupine Optimizer (ICPO), which can dynamically balance global and local exploitation. Furthermore, a bearing fault diagnosis method named ICPO-FMD is constructed, wherein the optimal parameter combination of K and L obtained using ICPO is provided to FMD in order to decompose bearing signals into a family of intrinsic mode functions (IMFs), and then fault sensitive components are extracted according to the proposed IMF screening principle. Finally, a reconstructed signal is obtained, followed by an envelope demodulation analysis. Experiments on simulation, laboratory and engineering signals demonstrate that the proposed method can accurately extract the fault characteristic frequency and its harmonics.