A novel adaptive fault diagnosis based on two-stage parameter optimization feature mode decomposition

The vibration signals of rolling bearings usually show significant nonstationarity and low SNR, which makes it difficult to extract fault characteristics effectively by traditional methods. As a new signal analysis method, feature mode decomposition (FMD) has excellent performance in fault diagnosis, but it has the problem of artificial parameter setting. Besides, its objective function correlated kurtosis (CK) was unstable under nonstationary conditions. To solve these problems, this study proposes a two-stage parameter optimization FMD method. First, the original fault signal is denoised using complete ensemble empirical mode decomposition with adaptive noise, and its frequency domain signal-to-noise ratio is evaluated. Second, designed a slope kurtosis error ratio (SKER) index. By combining short-time kurtosis error and slope entropy, the traditional CK is replaced. Third, a two-stage parameter adaptive optimization strategy is proposed, an improved tornado optimizer with Coriolis force is used to search the optimal filter parameters globally, aiming at minimizing SKER. Meanwhile, by combining energy ratio, CK gain, and envelope harmonic ratio, a comprehensive scoring function was constructed to dynamically determine the number of modes. Finally, the envelope spectrum of each mode component is calculated to realize the fault identification of rolling bearings. The effectiveness of this method in fault diagnosis is verified by the experimental analysis of single fault signal and compound fault signal. At the same time, the superiority and robustness of this method in fault diagnosis are further proved by comparing with variational mode decomposition and FMD.