A fault diagnosis approach for rolling bearing based on Fourier transform multi-filter decomposition and symbolic dynamic entropy

2019 ◽  
Author(s):  
Hao Pan ◽  
Li Kong ◽  
Kaibo Zhou ◽  
Jie Liu ◽  
Xiaoran Chen ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Symbolic Dynamic ◽  
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 Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Hongmei Liu ◽  
Lianfeng Li ◽  
Jian Ma
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Short Time Fourier Transform ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Short Time

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.

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 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

Health condition identification for rolling bearing based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine–based binary tree

2020 ◽  
pp. 147592172092397
Author(s):  
Cheng Yang ◽  
Minping Jia
Keyword(s):  
Fault Diagnosis ◽  
Least Squares ◽  
Binary Tree ◽  
Rolling Bearing ◽  
Health Condition ◽  
Symbolic Dynamic ◽  
Superior Performance ◽  
Vibration Data ◽  
Hierarchical Multiscale ◽  
Center Distance

Bearing health condition identification plays a crucial role in guaranteeing maximum productivity and reducing maintenance costs. In this article, a novel tensorial feature extraction approach called hierarchical multiscale symbolic dynamic entropy is developed, which can be used to assess the dynamic characteristic of the measured vibration data at different hierarchical layers and different scales. Besides, the influence of parameters in hierarchical multiscale symbolic dynamic entropy is investigated so as to select the optimal parameters. Then, a new multi-fault classifier called least squares support tensor machine–based binary tree is presented to achieve the fault identification automatically. In the least squares support tensor machine–based binary tree method, the divisibility measure strategy is constructed by two new separability measures (i.e. the average center distance of samples in one class, the center distance of samples between sub-class and global class). Finally, a novel intelligent fault diagnosis scheme based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine–based binary tree is developed, which is applied to analyze the experimental data of rolling bearing. The results indicate that the proposed scheme has a superior performance in health condition identification. Compared with the existing symbolic dynamic entropy–based fault diagnosis methods, the proposed method has higher diagnostic accuracy and better stability.

Sign in / Sign up

Export Citation Format

Share Document