Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network

With the aim of automatic recognition of weak faults in hydraulic systems, this paper proposes an identification method based on multi-scale permutation entropy feature extraction of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN). In this method, the leakage fault signal is first decomposed by empirical mode decomposition (EMD), and fault-sensitive IMF components are screened by adopting the correlation analysis method. The multi-scale entropy feature of each screened IMF is then extracted and features closely related to the weak fault information are then obtained. Finally, DBN is used for identification of fault diagnosis. Experimental results prove that this identification method has an ideal recognition effect. It can accurately judge whether there is a leakage fault, determine the degree of severity of the fault, and can diagnose and analyze hydraulic weak faults in general.

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Railway rolling bearing fault diagnosis based on multi-scale intrinsic mode function permutation entropy and extreme learning machine classifier

Advances in Mechanical Engineering ◽

10.1177/1687814016676157 ◽

2016 ◽

Vol 8 (10) ◽

pp. 168781401667615 ◽

Cited By ~ 5

Author(s):

Dechen Yao ◽

Jianwei Yang ◽

Yongliang Bai ◽

Xiaoqing Cheng

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Rolling Bearing ◽

Intrinsic Mode Function ◽

Permutation Entropy ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Mode Function ◽

Learning Machine

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Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder

Applied Sciences ◽

10.3390/app9132743 ◽

2019 ◽

Vol 9 (13) ◽

pp. 2743 ◽

Cited By ~ 6

Author(s):

Dai ◽

Tang ◽

Shao ◽

Huang ◽

Wang

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Ensemble Empirical Mode Decomposition ◽

Health Condition ◽

Intrinsic Mode Function ◽

Permutation Entropy ◽

Diagnostic Model ◽

Denoising Autoencoder ◽

Essential Information ◽

Mode Function

Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE) which combines sparse autoencoder (SAE) and denoising autoencoder (DAE) is proposed in this paper. The sparse criterion in SAE, corrupting operation in DAE and reasonable designing of the stack order of autoencoders help to mine essential information of the input and improve fault pattern classification robustness. In order to provide better input features for the constructed network, the raw non-stationary and nonlinear vibration signals are processed with ensemble empirical mode decomposition (EEMD) and multiscale permutation entropy (MPE). MPE features which are extracted based on both the selected characteristic frequency-related intrinsic mode function components (IMFs) and the raw signal, are used as low-level feature for the input of the proposed diagnostic model for health condition recognition and classification. Two experiments based on the Case Western Reserve University (CWRU) dataset and the measurement dataset from laboratory were conducted, and results demonstrate the effectiveness of the proposed method and highlight its excellent performance relative to existing methods.

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Feature Extraction of Ship Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

10.20944/preprints201611.0052.v1 ◽

2016 ◽

Author(s):

Yu-Xing Li ◽

Ya-An Li ◽

Zhe Chen ◽

Xiao Chen

Keyword(s):

Feature Extraction ◽

Empirical Mode Decomposition ◽

Characteristic Parameter ◽

Intrinsic Mode Function ◽

Permutation Entropy ◽

Intrinsic Mode Functions ◽

Characteristic Parameters ◽

Radiated Noise ◽

Mode Function ◽

Mode Decomposition

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

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Feature Extraction of Ship-Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

Entropy ◽

10.3390/e18110393 ◽

2016 ◽

Vol 18 (11) ◽

pp. 393 ◽

Cited By ~ 26

Author(s):

Yu-Xing Li ◽

Ya-An Li ◽

Zhe Chen ◽

Xiao Chen

Keyword(s):

Feature Extraction ◽

Intrinsic Mode Function ◽

Permutation Entropy ◽

Radiated Noise ◽

Mode Function

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Nonlinear System Identification Method Based on Improved Deep Belief Network

2018 Chinese Automation Congress (CAC) ◽

10.1109/cac.2018.8623373 ◽

2018 ◽

Author(s):

Yang Man ◽

Liu Ding ◽

Zhao Xiaoguo

Keyword(s):

System Identification ◽

Nonlinear System ◽

Nonlinear System Identification ◽

Deep Belief Network ◽

Belief Network ◽

System Identification Method ◽

Identification Method

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Gesture Recognition Based on Multiscale Singular Value Entropy and Deep Belief Network

Sensors ◽

10.3390/s21010119 ◽

2020 ◽

Vol 21 (1) ◽

pp. 119

Author(s):

Wenguo Li ◽

Zhizeng Luo ◽

Yan Jin ◽

Xugang Xi

Keyword(s):

Gesture Recognition ◽

Language Translation ◽

Singular Value ◽

Deep Belief Network ◽

Permutation Entropy ◽

Multiple Time ◽

Belief Network ◽

Time Frequency ◽

S Transform ◽

Semg Signals

As an important research direction of human–computer interaction technology, gesture recognition is the key to realizing sign language translation. To improve the accuracy of gesture recognition, a new gesture recognition method based on four channel surface electromyography (sEMG) signals is proposed. First, the S-transform is applied to four channel sEMG signals to enhance the time-frequency detail characteristics of the signals. Then, multiscale singular value decomposition is applied to the multiple time-frequency matrix output of S-transform to obtain the time-frequency joint features with better robustness. The corresponding singular value permutation entropy is calculated as the eigenvalue to effectively reduce the dimension of multiple eigenvectors. The gesture features are used as input into the deep belief network for classification, and nine kinds of gestures are recognized with an average accuracy of 93.33%. Experimental results show that the multiscale singular value permutation entropy feature is especially suitable for the pattern classification of the deep belief network.

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Fault-cause identification method based on adaptive deep belief network and time–frequency characteristics of travelling wave

IET Generation Transmission & Distribution ◽

10.1049/iet-gtd.2018.6334 ◽

2019 ◽

Vol 13 (5) ◽

pp. 724-732 ◽

Cited By ~ 4

Author(s):

Hanqing Liang ◽

Yadong Liu ◽

Gehao Sheng ◽

Xiuchen Jiang

Keyword(s):

Travelling Wave ◽

Frequency Characteristics ◽

Deep Belief Network ◽

Belief Network ◽

Time Frequency ◽

Identification Method

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Application of Adaptive Local Iterative Filtering and Permutation Entropy in Gear Fault Recognition

Mathematical Problems in Engineering ◽

10.1155/2021/8049516 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Wenbin Zhang ◽

Yun Wang ◽

Yushuo Tan ◽

Dewei Guo ◽

Yasong Pu

Keyword(s):

Vibration Signal ◽

Intrinsic Mode Function ◽

Gear System ◽

Permutation Entropy ◽

Intrinsic Mode Functions ◽

Mode Function ◽

Gray Correlation Degree ◽

Correlation Degree ◽

Gear Fault ◽

Iterative Filtering

In this paper, a fault identification method combining adaptive local iterative filtering and permutation entropy is proposed. The adaptive local iterative filtering can decompose the nonstationary signal into a finite number of stationary intrinsic mode functions. And the experiment gear fault data are decomposed into several intrinsic mode functions by this method. Then, using the permutation entropy to calculate each intrinsic mode function, it is found that the permutation entropy of the first several intrinsic mode functions can represent the characteristics of different fault types, and the permutation entropy of the intrinsic mode function corresponding to the rotating frequency signal of the gear system could be the boundary. Finally, the fault type of gear is identified by calculating the gray correlation degree of permutation entropy of essential mode function of vibration signal decomposition under different working conditions. The example analysis results show that the proposed method can be effectively applied to the fault diagnosis of the gear system.

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