An enhanced MCKD based on built-in information for gearbox fault diagnosis under speed-varying condition

As built-in information, encoder data have demonstrated distinct advantages over vibration signals in the field of mechanical fault diagnosis. However, mechanical equipment in actual industrial settings operates under speed-varying and nonstationary conditions. This poses tremendous challenges to the direct application of conventional feature extraction methods on encoder information for equipment fault diagnosis under speed-varying conditions, especially without the assistance of additional external speed sensors. Motivated by this issue, this article proposes an enhanced maximum correlated kurtosis (CK) deconvolution (EMCKD) method for gearbox fault diagnosis using built-in encoder information under speed-varying conditions. First, the instantaneous angular acceleration signal (after second-order differencing) is resampled based on the encoder position sequence as a reference to eliminate the influence of speed fluctuations. This approach overcomes the limitation of traditional monitoring techniques that rely on extra speed sensors to provide rotational speed information. Second, time-domain synchronous averaging is proposed as the cycle estimation method for maximum CK deconvolution. This method effectively improves the accuracy of period estimation during each iteration, thereby enhancing the efficiency and diagnostic precision of EMCKD when processing complex encoder information. Finally, the proposed method is validated using both simulated encoder data and actual fault data from planetary gearboxes. Comprehensive quantitative analyses demonstrate that EMCKD exhibits superior parameter stability and robust anti-noise capability even under extremely low signal-to-noise ratios. Furthermore, two distinct experimental cases, external gear ring and planetary gear faults, are conducted, and the results indicate that the proposed method outperforms traditional methods by a significant margin.