Planetary gearbox is a key component in modern industry. A sudden failure may cause disastrous consequences. Thus, accurately acquiring the fault severity can be of importance. Diversity entropy emerges as a promising feature extraction tool for monitoring the health condition. However, the original diversity entropy has the defect that the data length of multiple time series will shorten at deep scales, resulting in unstable complexity estimation at high scale. To overcome this defect, a new feature extraction method has been proposed named refined composite multiscale diversity entropy (RCMDE). The proposed RCMDE method combines moving average windows under each scale factor and the refined state probability to improve the statistical reliability, which allows the diversity entropy to explore more refined fault information hidden at deeper scales. The simulation and experiment results proved that the proposed method has the highest diagnostic accuracy with the best stability in fault severity identification of planetary gearbox.
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