A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM

Degradation State Identification of Cracked Ultrasonic Motor by Means of Fault Feature Extraction Method

“…According to Equation (18), the more homogeneous the energy of ISCs, the greater the entropy. Figure 10 presents a reasonable trend that the LCD energy entropy increases with the fault degradation.…”

“…Yu et al applied the timefrequency entropy method to gear fault diagnosis with the help of Hilbert-Huang transform [15]. Among the applications of entropy, it is worthy noting that permutation entropy (PE) has been widely used in the mutation detection of electroencephalogram, heart interbeat signal, and mechanical signal in past few years [16][17][18]. PE not only reflects the complexity of one-dimensional time series but also has a high sensitivity to information influenced by dynamic changes in complex systems [19].…”

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

“…Zhang et al [70] singular value decomposition + permutation entropy 8 Wang et al [71] wavelet packet transform + permutation entropy 9 Zhao et al [72] wavelet packet decomposition + multiscale permutation entropy Fu et al [73] variational mode decomposition + permutation entropy Yan et al [74] improved variational mode decomposition + instantaneous energy distribution-permutation entropy Yasir et al [75] multi-scale permutation entropy Tian et al [76] permutation entropy + manifold-based dynamic time warping Lv et al [77] permutation entropy Zheng et al [78] support vector machine + multiscale permutation entropy Xu et al [79] compound multiscale permutation entropy + particle swarm optimization-support vector machine Li et al [80] improved multiscale permutation + least squares support vector machine Huo et al [81] permutation entropy + Laplacian score + support vector machine Li et al [82] permutation entropy + improved support vector machine Dong et al [83] time-shift multi-scale weighted permutation entropy + gray wolf optimized support vector machine Zhou et al [84] weighted permutation entropy + improved support vector machine ensemble classifier Tiwari et al [85] adaptive neuro fuzzy classifier + multiscale permutation entropy Yi et al [86] tensor-based singular spectrum algorithm + permutation entropy Zhang et al [87] feature space reconstruction + multiscale permutation entropy Zheng et al [88] multi-scale weighted permutation entropy + extreme learning machine Xue et al [89] two-step scheme based on permutation entropy + random forest One typical method is that the wavelet packet transform and decomposition are combined with permutation entropy to enhance the ability of feature extraction [71,72]. The method based on wavelet analysis is effective to extract features contained in the weak transient signal.…”

“…Xu et al [79] combine compound multiscale permutation entropy with support vector machine and particle swarm optimization for bearing fault diagnosis. A method based on improved multiscale permutation entropy, laplacian score, and least squares support vector machine-quantum behaved particle swarm optimization is proposed in [80]. Similar to this idea, Huo et al [81] propose a method by integrating the fine-to-coarse multiscale permutation entropy, laplacian score and support vector machine.…”

Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy

“…Zheng [39] proposed an improved MPE method, called generalized composite multiscale permutation entropy, to solve the drawback of the coarse graining process in MPE. To improve the trend and stability of the MPE method, composite multiscale permutation entropy (CMPE) was put forward by improving the coarse-grained procedure and obtaining several PE values to describe in one same scale [40,41]. Si [42] applied CMPE for accurate cutting state recognition of a shearer and Yin [43] combined CMPE and WPT for arc fault detection; they all achieved expected results, which proved the effectiveness of feature extraction by CMPE.In this paper, a novel method for the fault diagnosis of rail vehicle axle-box bearings is proposed, based on frequency-domain energy feature reconstruction (EFR) and CMPE.…”