A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection

Li, Yongbo; Yang, Yuantao; Li, Guoyan; Xu, Min; Huang, Wenhu

doi:10.1016/j.ymssp.2016.12.040

Cited by 217 publications

(120 citation statements)

References 40 publications

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“…Currently, there are several methods to measure complexity of time series, such as the permutation entropy (PE) [32], sample entropy (SampEn) [33], spectral entropy (SE) [34], and C 0 algorithms [35]. It should be noted out that complexity of chaotic systems is mainly estimated based on the original time series, and complexity analysis of nonlinear symbol sequence has aroused interests of researchers [36,37]. Meanwhile, there are many kinds of pseudorandom sequence generation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Chaos and Symbol Complexity in a Conformable Fractional‐Order Memcapacitor System

Banerjee

2018

Complexity

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Application of conformable fractional calculus in nonlinear dynamics is a new topic, and it has received increasing interests in recent years. In this paper, numerical solution of a conformable fractional nonlinear system is obtained based on the conformable differential transform method. Dynamics of a conformable fractional memcapacitor (CFM) system is analyzed by means of bifurcation diagram and Lyapunov characteristic exponents (LCEs). Rich dynamics is found, and coexisting attractors and transient state are observed. Symbol complexity of the CFM system is estimated by employing the symbolic entropy (SybEn) algorithm, symbolic spectral entropy (SybSEn) algorithm, and symbolic C 0 (SybC 0 ) algorithm. It shows that pseudorandom sequences generated by the system have high complexity and pass the rigorous NIST test. Results demonstrate that the conformable memcapacitor nonlinear system can also be a good model for real applications.

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Section: Introductionmentioning

confidence: 99%

Chaos and Symbol Complexity in a Conformable Fractional‐Order Memcapacitor System

Banerjee

2018

Complexity

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“…However, a precise complexity analysis relies heavily on time sequence with sufficient samples. MMSE was proposed for complexity measurement of short-term time sequence [18] and has been applied for signal complexity analysis [24,25]. In this work, entropy features were extracted directly from wavelet coefficients.…”

Section: Modified Multi-scale Entropymentioning

confidence: 99%

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Jiang

Zhang

et al. 2019

Applied Sciences

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With the development of pervasive sensing and machine learning technologies, automated epileptic seizure detection based on electroencephalogram (EEG) signals has provided tremendous support for the lives of epileptic patients. Discrete wavelet transform (DWT) is an effective method for time-frequency analysis of EEG and has been used for seizure detection in daily healthcare monitoring systems. However, the shift variance, the lack of directionality and the substantial aliasing, limit the effects of DWT in some applications. Dual-tree discrete wavelet transform (DTDWT) can overcome those drawbacks but may increase information redundancy. For classification tasks with small dataset sizes, such redundancy can greatly reduce learning efficiency and model performance. In this work, we proposed a novel redundancy removed DTDWT (RR-DTDWT) framework for automated seizure detection. Energy and modified multi-scale entropy (MMSE) features in a dual tree wavelet domain were extracted to construct a complete picture of mental states. To the best of our knowledge, this is the first study to employ MMSE as an indicator of epileptic seizures. Moreover, a compact EEG representation can be obtained after removing useless information redundancy (redundancy between wavelet trees, adjacent channels and entropy scales) by a general auto-weighted feature selection framework via global redundancy minimization (AGRM). Through validation on Bonn and CHB-MIT databases, the proposed RR-DTDWT method can achieve better performance than previous studies.

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“…Besides, as entropy is effective in detection the dynamic characteristics of time series, some methods use entropy in feature extraction and selection. Modified multi-scale symbolic dynamic entropy (MMSDE) [13] and modified hierarchical permutation entropy (MHPE) [14] were proposed to extract features for gearbox fault diagnosis. Zhao et al [15] combined the EEMD and multi-scale fuzzy entropy to extract more discriminative features.…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering

Qian¹,

Li²,

Wang³

et al. 2018

J. vibroeng.

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Traditional intelligent fault diagnosis methods take advantage of diagnostic expertise but are labor-intensive and time-consuming. Among various unsupervised feature extraction methods, sparse filtering computes fast and has less hyperparameters. However, the standard sparse filtering has poor generalization ability and the extracted features are not so discriminative by only constraining the sparsity of the feature matrix. Therefore, an improved sparse filtering with L1 regularization (L1SF) is proposed to improve the generalization ability by improving the sparsity of the weight matrix, which can extract more discriminative features. Based on Fourier transformation (FFT), L1SF, softmax regression, a new three-stage intelligent fault diagnosis method of rotating machinery is developed. It first transforms time-domain samples into frequency-domain samples by FFT, then extracts features in L1-regularized sparse filtering and finally identifies the health condition in softmax regression. Meanwhile, we propose employing different activation functions in the optimization of L1SF and feedforward for considering their different requirements of the non-saturating and anti-noise properties. Furthermore, the effectiveness of the proposed method is verified by a bearing dataset and a gearbox dataset respectively. Through comparisons with the standard sparse filtering and L2-regularized sparse filtering, the superiority of the proposed method is verified. Finally, an interpretation of the weight matrix is given and two useful sparse properties of weight matrix are defined, which explain the effectiveness of L1SF. . His current research interests include rotating machinery fault diagnosis and mechanical signal and information processing.

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A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection

Cited by 217 publications

References 40 publications

Chaos and Symbol Complexity in a Conformable Fractional‐Order Memcapacitor System

Chaos and Symbol Complexity in a Conformable Fractional‐Order Memcapacitor System

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering

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