Abstract
Multi-scale permutation entropy (MPE) is a statistic indicator to detect nonlinear dynamic changes in time series, which has merits of high calculation efficiency, good robust ability, and independence from prior knowledge, etc. However, the performance of MPE is dependent on the parameter selection of embedding dimension and time delay. To complete the automatic parameter selection of MPE, a novel parameter optimization strategy of MPE is proposed, namely optimized multi-scale permutation entropy (OMPE). In the OMPE method, an improved Cao method is proposed to adaptively select the embedding dimension. Meanwhile, the time delay is determined based on mutual information. To verify the effectiveness of OMPE method, a simulated signal and two experimental signals are used for validation. Results demonstrate that the proposed OMPE method has a better feature extraction ability comparing with existing MPE methods.
Highlights
Permutation entropy [1,2], as a statistic indicator to detect nonlinear dynamic changes, has been widely used in the fault feature extraction of rotating machinery [3,4,5,6]
Conditions are the same as follows: (1) 10 scales multi-scale permutation entropy (MPE) are used as input features; (2) the k-nearest is used as classifier to identify the different fault types; (3) the length of each sample is 2048 points; neighbor (KNN) classifier [29,30] is used as classifier to identify the different fault types; (3) the
This paper proposes a parameter selection approach for MPE, namely optimized MPE (OMPE)
Summary
Permutation entropy [1,2], as a statistic indicator to detect nonlinear dynamic changes, has been widely used in the fault feature extraction of rotating machinery [3,4,5,6]. Wu et al [15,16] used MPE to extract the fault features and applied a support vector machine to identify the bearing fault types. Yao et al [19] employed the MPE to describe the fault characteristics and used the extreme learning machine for bearing pattern identifications These works have successfully applied MPE in fault diagnosis of rotating machinery. The parameter selection of embedding dimension m and time delay τ plays an important role in the MPE method [2]. Aiming to automatically select the optimum parameters of MPE, a novel parameter optimization strategy of MPE is proposed in this paper. We call this method optimized multi-scale permutation entropy (OMPE).
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