A new bearing fault diagnosis approach combining sensitive statistical features with improved multiscale permutation entropy method

Obtaining the sensitive feature vectors from the vibration signal is crucial to indicate the bearing's actual condition. Most often, weak feature vectors are the consequence of heavy noise in the original signal and the incompatibility of the methods to deal with the nonlinear and non-stationary nature of vibration signals. In this paper, the first issue is addressed by employing a complementary ensemble empirical mode decomposition method. A new method based on improved multiscale permutation entropy method and dominant statistical parameters is proposed to deal with the second issue. The proposed approach's denoising capability is first verified on an amplitude modulated and frequency modulated simulated signal. On the experimental front, the proposed method is investigated under a wide range of operating conditions to simultaneously recognize bearing fault type and severity. Since the experimental investigation includes identifying dominant statistical parameters and classifying different bearing faults, a recent method named XGBoost is explored comprehensively. The results show that the classification accuracy with features extracted by the proposed method exceeds 3% to 18% compared to features extracted by other state-of-the-art permutation-based feature extraction methods. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper focuses on obtaining sensitive feature vectors from vibration signals to indicate the bearing's actual condition, addressing issues related to heavy noise and the nonlinearity of vibration signals. A new method using improved multiscale permutation entropy method and dominant statistical parameters is proposed. Experimental results show that the proposed method outperforms other state-of-the-art feature extraction methods in terms of classification accuracy.