Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

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A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Sensors ◽

10.3390/s20113105 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3105 ◽

Cited By ~ 1

Author(s):

Cong Dai Nguyen ◽

Alexander Prosvirin ◽

Jong-Myon Kim

Keyword(s):

Fault Diagnosis ◽

Reference Signal ◽

Vibration Signal ◽

Support Vector ◽

Health States ◽

Informative Signal ◽

Vibration Signals ◽

Optimal Output ◽

Gear Fault ◽

Diagnosis Method

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

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A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Entropy ◽

10.3390/e23010128 ◽

2021 ◽

Vol 23 (1) ◽

pp. 128

Author(s):

Chenbo Xi ◽

Guangyou Yang ◽

Lang Liu ◽

Hongyuan Jiang ◽

Xuehai Chen

Keyword(s):

Fault Diagnosis ◽

Single Channel ◽

Vibration Signal ◽

Rotating Machinery ◽

Support Vector ◽

Data Set ◽

Rotary Machine ◽

Gear Fault ◽

Diagnosis Method ◽

Low Sensitivity

In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

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Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis

International Journal of Rotating Machinery ◽

10.1155/2017/2384184 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Jun Shuai ◽

Changqing Shen ◽

Zhongkui Zhu

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Morphological Feature ◽

Morphological Analysis ◽

Vibration Signal ◽

Health Condition ◽

Support Vector ◽

Intelligent Fault Diagnosis ◽

Data Set ◽

Significant Step

Numerous studies on fault diagnosis have been conducted in recent years because the timely and correct detection of machine fault effectively minimizes the damage resulting in the unexpected breakdown of machineries. The mathematical morphological analysis has been performed to denoise raw signal. However, the improper choice of the length of the structure element (SE) will substantially influence the effectiveness of fault feature extraction. Moreover, the classification of fault type is a significant step in intelligent fault diagnosis, and many techniques have already been developed, such as support vector machine (SVM). This study proposes an intelligent fault diagnosis strategy that combines the extraction of morphological feature and support vector regression (SVR) classifier. The vibration signal is first processed using various scales of morphological analysis, where the length of SE is determined adaptively. Thereafter, nine statistical features are extracted from the processed signal. Lastly, an SVR classifier is used to identify the health condition of the machinery. The effectiveness of the proposed scheme is validated using the data set from a bearing test rig. Results show the high accuracy of the proposed method despite the influence of noise.

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Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

Shock and Vibration ◽

10.1155/2018/1063645 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Fan Jiang ◽

Zhencai Zhu ◽

Wei Li ◽

Bo Wu ◽

Zhe Tong ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Optimal Number ◽

Permutation Entropy ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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Mechanical Fault Diagnosis for HV Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine

Mathematical Problems in Engineering ◽

10.1155/2015/101757 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 7

Author(s):

Jianfeng Zhang ◽

Mingliang Liu ◽

Keqi Wang ◽

Laijun Sun

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Circuit Breaker ◽

Vibration Signal ◽

Ensemble Empirical Mode Decomposition ◽

Support Vector ◽

Time Segment ◽

Vibration Signals ◽

Equal Time ◽

Mode Decomposition

During the operation process of the high voltage circuit breaker, the changes of vibration signals can reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD). Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM) to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.

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A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Entropy ◽

10.3390/e23101319 ◽

2021 ◽

Vol 23 (10) ◽

pp. 1319

Author(s):

Haikun Shang ◽

Junyan Xu ◽

Yucai Li ◽

Wei Lin ◽

Jinjuan Wang

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Extraction Method ◽

Vibration Signal ◽

Practical Significance ◽

Stable Operation ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Feature Extraction Method ◽

Multi Scale

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

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Feature Mapping Techniques for Improving the Performance of Fault Diagnosis of Synchronous Generator

International Journal of Prognostics and Health Management ◽

10.36001/ijphm.2015.v6i2.2256 ◽

2020 ◽

Vol 6 (2) ◽

Author(s):

R. Gopinath ◽

C. Santhosh Kumar ◽

K. Vishnuprasad ◽

K. I. Ramachandran

Keyword(s):

Fault Diagnosis ◽

Sparse Coding ◽

Learning Algorithm ◽

Dimensional Space ◽

Synchronous Generator ◽

Identification Accuracy ◽

Support Vector ◽

Feature Mapping ◽

Data Set ◽

Mapping Techniques

Support vector machine (SVM) is a popular machine learning algorithm used extensively in machine fault diagnosis. In this paper, linear, radial basis function (RBF), polynomial, and sigmoid kernels are experimented to diagnose inter-turn faults in a 3kVA synchronous generator. From the preliminary results, it is observed that the performance of the baseline systemis not satisfactory since the statistical features are nonlinear and does not match to the kernels used. In this work, the features are linearized to a higher dimensional space to improve the performance of fault diagnosis system for a synchronous generator using feature mapping techniques, sparse coding and locality constrained linear coding (LLC). Experiments and results show that LLC is superior to sparse coding for improving the performance of fault diagnosis of a synchronous generator. For the balanced data set, LLC improves the overall fault identification accuracy of the baseline RBF system by 22.56%, 18.43% and 17.05% for the R, Y and Bphase faults respectively.

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A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406216677102 ◽

2016 ◽

Vol 232 (2) ◽

pp. 369-380 ◽

Cited By ~ 20

Author(s):

Chao Zhang ◽

Zhongxiao Peng ◽

Shuai Chen ◽

Zhixiong Li ◽

Jianguo Wang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Vibration Signal ◽

Dynamic State ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition ◽

Diagnosis Method

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

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Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input

Sensors ◽

10.3390/s19092000 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2000 ◽

Cited By ~ 8

Author(s):

Dongdong Zhao ◽

Feng Liu ◽

He Meng

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Fault Classification ◽

Generative Adversarial Networks ◽

Data Set ◽

Vibration Signals ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Training Samples ◽

Public Data

The bearing is a component of the support shaft that guides the rotational movement of the shaft, widely used in the mechanical industry and also called a mechanical joint. In bearing fault diagnosis, the accuracy much depends on the feature extraction, which always needs a lot of training samples and classification in the commonly used methods. Neural networks are good at latent feature extraction and fault classification, however, they have problems with instability and over-fitting, and more labeled samples must be trained. Switchable normalization and semi-supervised learning are introduced to solve the above obstacles in this paper, which proposes a novel bearing fault diagnosis method based on switchable normalization semi-supervised generative adversarial networks (SN-SSGAN) with 1-dimensional representation of vibration signals as input. Experimental results showed that the proposed method has a desirable 99.93% classification accuracy in the case of less labeled data from the public data set of West Reserve University, which is better than the state-of-the-art methods.

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Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification

The Scientific World JOURNAL ◽

10.1155/2014/145807 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Pengfei Li ◽

Yongying Jiang ◽

Jiawei Xiang

Keyword(s):

Fault Diagnosis ◽

Wavelet Packet ◽

Hybrid Approach ◽

Vibration Signal ◽

Rolling Bearing ◽

Support Vector ◽

Gear Fault ◽

Mechanical Faults ◽

Energy Ratios ◽

Robust To Noise

To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples.

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