Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.

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A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

Shock and Vibration ◽

10.1155/2014/825825 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 25

Author(s):

HungLinh Ao ◽

Junsheng Cheng ◽

Kenli Li ◽

Tung Khac Truong

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Roller Bearing ◽

Local Characteristic ◽

Support Vector ◽

Svm Classifier ◽

Outer Race ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

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Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

Shock and Vibration ◽

10.1155/2009/519502 ◽

2009 ◽

Vol 16 (1) ◽

pp. 89-98 ◽

Cited By ~ 55

Author(s):

Junsheng Cheng ◽

Dejie Yu ◽

Jiashi Tang ◽

Yu Yang

Keyword(s):

Fault Diagnosis ◽

Feature Vector ◽

Roller Bearing ◽

Vibration Signal ◽

Singular Values ◽

Rotating Machinery ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition

Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

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Rolling bearing fault diagnosis of PSO–LSSVM based on CEEMD entropy fusion

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2019-0114 ◽

2020 ◽

Vol 44 (3) ◽

pp. 405-418

Author(s):

Shuzhi Gao ◽

Tianchi Li ◽

Yimin Zhang

Keyword(s):

Fault Diagnosis ◽

Roller Bearing ◽

Vibration Signal ◽

Rolling Bearing ◽

Fault Classification ◽

Kernel Principal Component Analysis ◽

Permutation Entropy ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Taking aim at the nonstationary nonlinearity of the rolling bearing vibration signal, a rolling bearing fault diagnosis method based on the entropy fusion feature of complementary ensemble empirical mode decomposition (CEEMD) is proposed in combination with information fusion theory. First, CEEMD of the vibration signal of the rolling bearing is performed. Then the signal is decomposed into the sum of several intrinsic mode functions (IMFs), and the singular entropy, energy entropy, and permutation entropy are obtained for the IMFs with fault features. Second, the feature extraction method of entropy fusion is proposed, and the three entropy data obtained are input into kernel principal component analysis (KPCA) for feature fusion and dimensionality reduction to obtain complementary features. Finally, the extracted features are imported into the particle swarm optimization (PSO) algorithm to optimize the least-squares support vector machine (LSSVM) for fault classification. Through experimental verification, the proposed method can be used for roller bearing fault diagnosis.

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The Fault Diagnosis Method for Roller Bearing with Harmonic Wavelet Packet and DT-SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.724.238 ◽

2015 ◽

Vol 724 ◽

pp. 238-241

Author(s):

Rui Pan ◽

Tao Xu ◽

Yong Liu

Keyword(s):

Fault Diagnosis ◽

Wavelet Packet ◽

Roller Bearing ◽

Vibration Signal ◽

Support Vector ◽

Time Frequency ◽

Vibration Data ◽

Electric Engineering ◽

Harmonic Wavelet ◽

Diagnosis Method

This paper studies the roller bearing fault diagnosis method with harmonic wavelet packet and Decision Tree-Support Vector Machine (DT-SVM). The harmonic wavelet packet possesses better performances for its box-shaped spectrum and unlimited subdivision compared with the conventional time-frequency feature exaction method. Firstly, the proposed method decomposes the roller bearing vibration signal with harmonic wavelet packet and extracts the feature energy with coefficients of each spectrum. After the feature energy is normalized, feature vector are available. Based on multi-level binary tree, this paper designs the multi-classification SVM model due to its superior nonlinear mapping capability. Three two-classifications are incorporated to diagnosis the roller bearing faults. Finally, the proposed method is illustrated with the vibration data from the roller bearing stand of electric engineering lab in case western reserve university. Experimental results illustrate the higher accuracy of the proposed method compared with conventional method.

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FAULT DIAGNOSIS BASED ON LMD-SVD AND INFORMATION-GEOMETRIC SUPPORT VECTOR MACHINE FOR HYDRAULIC PUMPS

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2015-0044 ◽

2015 ◽

Vol 39 (3) ◽

pp. 569-580 ◽

Cited By ~ 3

Author(s):

Ye Tian ◽

Chen Lu ◽

Zhipeng Wang ◽

Zili Wang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Small Sample ◽

Support Vector ◽

Svm Classifier ◽

Health States ◽

Diagnosis Method ◽

Stationary Vibration ◽

Local Mean ◽

Value Decomposition

This study proposes a fault diagnosis method for hydraulic pumps based on local mean decomposition (LMD), singular value decomposition (SVD), and information-geometric support vector machine (IG-SVM). First, the nonlinear and non-stationary vibration signals are decomposed using LMD into several product functions (PFs). Then, the PFs are processed by SVD to obtain more stable and compact feature vectors. Finally, the health states are identified by an IG-SVM classifier, which is less-dependent on the selected kernel function and parameters than SVM. In addition, the comparisons between LMD, EMD, and WPD demonstrate the superiority of LMD in feature extraction. Compared with SVM and BP neural network, IG-SVM shows higher classification accuracy and computational efficiency in dealing with small-sample fault diagnosis. From the experimental results, it was concluded that the proposed method can effectively realize fault diagnosis for hydraulic pumps under small-sample conditions.

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Fault diagnosis of a wind turbine rolling bearing using adaptive local iterative filtering and singular value decomposition

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331216644041 ◽

2016 ◽

Vol 39 (11) ◽

pp. 1643-1648 ◽

Cited By ~ 7

Author(s):

Xueli An ◽

Hongtao Zeng ◽

Weiwei Yang ◽

Xuemin An

Keyword(s):

Fault Diagnosis ◽

Singular Value Decomposition ◽

Wind Turbine ◽

Multiple Scales ◽

Roller Bearing ◽

Vibration Signal ◽

Singular Value ◽

Feature Vectors ◽

Iterative Filtering ◽

Value Decomposition

Adaptive local iterative filtering (ALIF) is a new signal decomposition method that uses the iterative filters strategy together with an adaptive and data-driven filter length selection to achieve the decomposition. The complexity of wind power generation systems means that the randomness and kinetic mutation behaviour of their vibration signals are demonstrated at different scales. Thus it is necessary to analyse the vibration signal across multiple scales. A method based on ALIF and singular value decomposition (SVD) was used for the fault diagnosis of a wind turbine roller bearing. The ALIF method is used to decompose the bearing vibration signal into several stable components. The components, which contain major fault information, are selected to build an initial feature vector matrix. The singular value of the matrix is computed as the feature vectors of each bearing fault. The feature vectors embody the characteristics of the vibration signal. The nearest neighbour algorithm is used as a classifier to identify faults in a roller bearing. Experimental data show that the proposed method can be used to identify roller bearing faults of a wind turbine.

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Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Entropy ◽

10.3390/e20080604 ◽

2018 ◽

Vol 20 (8) ◽

pp. 604 ◽

Cited By ~ 8

Author(s):

Wei He ◽

Yigang He ◽

Bing Li ◽

Chaolong Zhang

Keyword(s):

Fault Diagnosis ◽

Analog Circuit ◽

Support Vector ◽

Svm Classifier ◽

Frequency Spectra ◽

Time Frequency ◽

Circuit Fault Diagnosis ◽

Value Decomposition ◽

Environment Noise ◽

Cross Wavelet

In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits’ defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.

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Fault Diagnosis of a Rolling Bearing Based on Adaptive Sparest Narrow-Band Decomposition and RefinedComposite Multiscale Dispersion Entropy

Entropy ◽

10.3390/e22040375 ◽

2020 ◽

Vol 22 (4) ◽

pp. 375

Author(s):

Songrong Luo ◽

Wenxian Yang ◽

Youxin Luo

Keyword(s):

Fault Diagnosis ◽

Fault Location ◽

Narrow Band ◽

Vibration Signal ◽

Rolling Bearing ◽

Support Vector ◽

Sensitive Information ◽

Time Frequency ◽

Fault Type ◽

Proposed Model

Condition monitoring and fault diagnosis of a rolling bearing is crucial to ensure the reliability and safety of a mechanical system. When local faults happen in a rolling bearing, the complexity of intrinsic oscillations of the vibration signals will change. Refined composite multiscale dispersion entropy (RCMDE) can quantify the complexity of time series quickly and effectively. To measure the complexity of intrinsic oscillations at different time scales, adaptive sparest narrow-band decomposition (ASNBD), as an improved adaptive sparest time frequency analysis (ASTFA), is introduced in this paper. Integrated, the ASNBD and RCMDE, a novel-fault diagnosis-model is proposed for a rolling bearing. Firstly, a vibration signal collected is decomposed into a number of intrinsic narrow-band components (INBCs) by the ASNBD to present the intrinsic modes of a vibration signal, and several relevant INBCs are prepared for feature extraction. Secondly, the RCMDE values are calculated as nonlinear measures to reveal the hidden fault-sensitive information. Thirdly, a basic Multi-Class Support Vector Machine (multiSVM) serves as a classifier to automatically identify the fault type and fault location. Finally, experimental analysis and comparison are made to verify the effectiveness and superiority of the proposed model. The results show that the RCMDE value lead to a larger difference between various states and the proposed model can achieve reliable and accurate fault diagnosis for a rolling bearing.

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Bearing Fault Diagnosis using Support Vector Machine with Genetic Algorithms based Optimization and K Fold Cross-Validation Method

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b2828.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 3242-3250

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Fault Diagnosis ◽

Cross Validation ◽

Vibration Signal ◽

Time Domain Analysis ◽

Actual Condition ◽

Support Vector ◽

Svm Classifier ◽

Outer Race

Moving component bearing is utilized to convey radial load and axial load or both just as. REB has nonlinear conduct make issue misalignment, surface waviness, fault happen at the inward race, external race, enclosure, ball or roller, so REB has a restricted life. Our concentration to evacuate fault diagnosis of bearing at the outer race has been investigating. For this purpose, REB vibration analysis is used. This paper present a support vector machine algorithm (SVM) approach with GA (Genetic algorithm) based optimization compare the result with SVM with cross-validation (CV) method along these lines, the information is processed correctly and an exact way. Time-domain Analysis, high pass and low pass filtering etc. used for feature extraction from vibration signal. Further, these feature extraction used as input to the SVM classifier. Support vector machine, a training given projected preparing information, the procedure yield perfect hyperplane. Feature extraction help to provides the actual condition of bearing. In this work, different signal processing techniques and process are used for fault diagnosis of bearing

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A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery

Shock and Vibration ◽

10.1155/2016/5141564 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Songrong Luo ◽

Junsheng Cheng ◽

Kexiang Wei

Keyword(s):

Fault Diagnosis ◽

Roller Bearing ◽

Vibration Signal ◽

Rotating Machinery ◽

Local Characteristic ◽

Neural Net ◽

Class Discrimination ◽

Model Based ◽

Feature Extraction And Selection ◽

Diagnosis Model

The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD) can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD) technique based local characteristic-scale decomposition (LCD) was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN) and mean impact value (MIV), ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.

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