scholarly journals Fault diagnosis approach of rolling bearing based on NA-MEMD and FRCMAC

2019 ◽  
Vol 2019 (13) ◽  
pp. 107-113
Author(s):  
Sheng Liu ◽  
Yue Sun ◽  
Lanyong Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Diagnosis Approach

scholarly journals A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4352 ◽  
Author(s):  
Xiaoan Yan ◽  
Ying Liu ◽  
Minping Jia
Keyword(s):  
Fault Diagnosis ◽  
Symplectic Geometry ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Relevance Vector Machine ◽  
Symbolic Dynamic ◽  
Bearing Fault ◽  
Fault Features ◽  
Bearing Vibration ◽  
Diagnosis Approach

The vibration signal induced by bearing local fault has strong nonstationary and nonlinear property, which indicates that the conventional methods are difficult to recognize bearing fault patterns effectively. Hence, to obtain an efficient diagnosis result, the paper proposes an intelligent fault diagnosis approach for rolling bearing integrated symplectic geometry mode decomposition (SGMD), improved multiscale symbolic dynamic entropy (IMSDE) and multiclass relevance vector machine (MRVM). Firstly, SGMD is employed to decompose the original bearing vibration signal into several symplectic geometry components (SGC), which is aimed at reconstructing the original bearing vibration signal and achieving the purpose of noise reduction. Secondly, the bat algorithm (BA)-based optimized IMSDE is presented to evaluate the complexity of reconstruction signal and extract bearing fault features, which can solve the problems of missing of partial fault information existing in the original multiscale symbolic dynamic entropy (MSDE). Finally, IMSDE-based bearing fault features are fed to MRVM for achieving the identification of bearing fault categories. The validity of the proposed method is verified by the experimental and contrastive analysis. The results show that our approach can precisely identify different fault patterns of rolling bearings. Moreover, our approach can achieve higher recognition accuracy than several existing methods involved in this paper. This study provides a new research idea for improvement of bearing fault identification.

scholarly journals A Fault Diagnosis Approach for Rolling Bearing Based on Wavelet Packet Decomposition and GMM-HMM

2019 ◽  
Vol 24 (2) ◽  
pp. 199-209 ◽  
Author(s):  
Liangpei Huang ◽  
Hua Huang ◽  
Yonghua Liu
Keyword(s):  
Fault Diagnosis ◽  
Failure Modes ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Model Parameters ◽  
Wavelet Packet Decomposition ◽  
Rolling Bearings ◽  
Fault Pattern ◽  
Diagnosis Approach

Considering frequency domain energy distribution differences of bearing vibration signal in the different failure modes, a rolling bearing fault pattern recognition method is proposed based on orthogonal wavelet packet decomposition and Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). The orthogonal three-layer wavelet packet decomposition is used to obtain wavelet packet decomposition coefficients from low frequency to high frequency. Rolling bearing raw vibration signals are firstly decomposed into the wavelet signals of different frequency bands, then different frequency band signals are reconstructed respectively to extract energy features, which form feature vectors as the model input of GMM-HMM. A large number of samples are trained to get model parameters for different bearing faults, then several groups of test data are adopted to verify GMM-HMMs so different fault types of rolling bearings are recognized. By calculating the current state appearance probability of monitoring data in GMM-HMMs, different failure patterns are recognized and evaluated from the maximum probability. Similarly, we establish GMM-HMMs for different grade fault samples and evaluated the performance degradation state. Test results show that the proposed fault diagnosis approach can identify accurately the fault pattern of rolling bearings and evaluate performance degradation of bearings.

scholarly journals Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

2021 ◽  
Author(s):  
B Peng ◽  
S Wan ◽  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Genetic Programming ◽  
Domain Knowledge ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Rotating Machinery ◽  
K Nearest Neighbors ◽  
Automatic Feature Extraction ◽  
Diagnosis Approach

IEEE Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this article, a novel diagnosis approach based on evolutionary learning, namely, automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery. To achieve this, a new program structure, a new function set, and a new terminal set are developed in AFECGP to allow it to detect important subband signals and extract and construct informative features, automatically and simultaneously. More important, AFECGP can produce a flexible number of features for classification. Having the generated features, k-Nearest Neighbors is employed to perform fault diagnosis. The performance of the AFECGP-based fault diagnosis approach is evaluated on four fault diagnosis datasets of varying difficulty and compared with 14 baseline methods. The results show that the proposed approach achieves better fault diagnosis accuracy on all the datasets than the competitive methods and can effectively identify different fault conditions of rolling bearing, gear, and rotor.

scholarly journals Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

2021 ◽  
Author(s):  
B Peng ◽  
S Wan ◽  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Genetic Programming ◽  
Domain Knowledge ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Rotating Machinery ◽  
K Nearest Neighbors ◽  
Automatic Feature Extraction ◽  
Diagnosis Approach

IEEE Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this article, a novel diagnosis approach based on evolutionary learning, namely, automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery. To achieve this, a new program structure, a new function set, and a new terminal set are developed in AFECGP to allow it to detect important subband signals and extract and construct informative features, automatically and simultaneously. More important, AFECGP can produce a flexible number of features for classification. Having the generated features, k-Nearest Neighbors is employed to perform fault diagnosis. The performance of the AFECGP-based fault diagnosis approach is evaluated on four fault diagnosis datasets of varying difficulty and compared with 14 baseline methods. The results show that the proposed approach achieves better fault diagnosis accuracy on all the datasets than the competitive methods and can effectively identify different fault conditions of rolling bearing, gear, and rotor.

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