Identification of gear fault by weighted Mahalanobis distance method based on multi-scale permutation entropy

Accurate identification of weak fault signals is critical for gear fault detection, yet particularly challenging. This study proposes a gear fault diagnosis method that utilizes Mutual Information (MI) and an Improved False Nearest Neighbor (IFNN) algorithm to optimize the delay time (τ) and embedding dimension (m) for Multiscale Permutation Entropy (MPE) calculation. The MPE values of various fault samples are computed using this optimized approach. The minimum Mahalanobis distance (min-MD_Maha) for each sample achieves a fault identification accuracy of 76.87%. Information entropy is then employed to extract useful information from different fault samples, serving as weights for the MDMaha. Experiments on gear pitting and wear faults validate the method. The weighted MDMaha significantly improves accuracy to 99.72%. The results demonstrate the superior effectiveness of the proposed weighted MDMaha-enhanced MPE framework in characterizing vibration signatures induced by gear faults.