Multivariate multiscale diversity entropy and its application to rotating machinery fault diagnosis

Entropy-based methods effectively assess the irregularity of time series to obtain underlying fault features from vibration signals. Multiscale diversity entropy (MDE) has been widely applied in feature extraction due to the high consistency between the entropy value and sequence complexity. However, its sequential processing of multivariate signals inherently ignores the valuable intersignal relationships, leading to a loss of critical information embedded in their correlations. To address this drawback, considering the dynamic and complex changes occurring both within and between channels, the multivariate MDE (MMDE) is proposed as a new complexity measurement method, which can capture their dynamic relationships and coupling feature of multivariate signals. Based on this, a fault diagnosis method for rotating machinery is proposed, which integrates MMDE and support vector machine. Both simulation and experimental results validate that the proposed MMDE excels at detecting dynamic changes in signals. Furthermore, the presented diagnostic method demonstrates superior capability over other feature extraction techniques.