Variational Embedding Multiscale Diversity Entropy for Fault Diagnosis of Large-Scale Machinery

The large-scale machinery generally requires multiple accelerometers for condition monitoring. The multichannel vibration signals carry a wealth of fault information. Synchronous fault feature extraction using multichannel signals will significantly improve the diagnostic performance. The multivariate entropy method is able to synchronously extract the fault features from multiple sensors, but how to recognize the single fault occurring on different channels remains unexplored. In this article, we propose a novel feature extraction method called variational embedding multiscale diversity entropy, which constructs the phase space with different structures. The proposed variational phase space construction strategy will generate a different probability distribution for each channel, which results in a better separability for multichannel feature extraction. Combined with the random forest classifier, a novel fault diagnosis scheme is developed for condition monitoring of the large-scale machinery. One simulated signal and two experimental data are designed to validate the effectiveness of the proposed strategy. The results demonstrate that the proposed method has the best multichannel feature extraction ability compared with three existing methods: multivariate multiscale sample entropy, multivariate multiscale fuzzy entropy, and multivariate multiscale permutation entropy.