An integrated health condition detection method for rolling bearings using time-shift multi-scale amplitude-aware permutation entropy and uniform phase empirical mode decomposition

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Abstract
Rolling bearings usually works in the condition with high speed, high temperature and high pressure, so their faults need to be detected and judged in time. This paper presents a new multi-scale entropy measurement algorithm called time-shift multi-scale amplitude-aware permutation entropy (TSMAAPE), combining time-shift coarse-grained method and multi-scale amplitude-aware permutation entropy (MAAPE) algorithm together creatively, making the extraction of useful information in time series more sufficient. In this paper, this new entropy algorithm is combined with uniform phase empirical mode decomposition algorithm, improved max-relevance and min-redundancy (ImRMR) algorithm and random forest algorithm, which are used to implement signal preprocessing, select features for dimensionality reduction, execute fault recognition and classification respectively, forming a new comprehensive detection method of bearing fault. The new comprehensive method includes two crucial parts: fault pre-detection and fault identification. Firstly, the threshold set based on TSMAAPE method is used to judge whether the bearing is in fault or not, if there is a fault, the subsequent steps are performed to identify and classify the fault. Compared with other traditional feature extraction methods, the proposed method is more effective and robust. The experimental results of two groups of bearing data show that the average accuracy of this method can reach 98.6%. The comprehensive detection method is of great significance for the rapid and accurate detection and identification of bearing faults in practical engineering applications.

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