Approach for Hydraulic Pump Fault Diagnosis Based on WPT-SVD and SVM

2015 ◽  
Vol 764-765 ◽  
pp. 191-197 ◽  
Author(s):  
Ye Tian ◽  
Chen Lu ◽  
Zi Li Wang
Keyword(s):  
Fault Diagnosis ◽  
High Efficiency ◽  
Small Sample ◽  
Singular Value ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Hydraulic Pump ◽  
Health States ◽  
Elman Neural Network

As the failure of a hydraulic pump is always instantaneous, the failure data are difficult to obtain. High-efficiency fault diagnosis under small-sample conditions for hydraulic pumps is urgently required in engineering applications. A fault diagnosis approach based on wavelet packet transform (WPT), singular value decomposition (SVD), and support vector machine (SVM) is proposed in this study. First, the nonlinear, non-stationary vibration signal of the hydraulic pump is decomposed into components by WPT. Second, singular value vectors are acquired as feature vectors by applying SVD to the components. Third, the health states of the hydraulic pumps are determined and classified with a SVM classifier. Furthermore, the SVM and Elman neural network classifiers are compared in terms of fault classification to demonstrate the superiority of SVM in dealing with small-sample problems. The results of the plunger pump rig test show that the proposed method can diagnose the faults of the hydraulic pump accurately even when the number of samples is small.

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

2015 ◽  
Vol 39 (3) ◽  
pp. 569-580 ◽  
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.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

scholarly journals Fault Diagnosis of Loader Gearbox Based on an ICA and SVM Algorithm

2019 ◽  
Vol 16 (23) ◽  
pp. 4868 ◽  
Author(s):  
Zhongxin Chen ◽  
Feng Zhao ◽  
Jun Zhou ◽  
Panling Huang ◽  
Xutao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Test Data ◽  
Sound Intensity ◽  
Original Data ◽  
National Standards ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Matlab Simulation ◽  
Independent Components

When a part of the loader’s gearbox fails, this can lead to equipment failure due to the complex internal structure and the interrelationship between the parts. Therefore, it is imperative to research an efficient strategy for transmission fault diagnosis. In this study, the non-contact characteristics of noise diagnosis using sound intensity probes were used to collect noise signals generated under gear breaking conditions. The independent component analysis (ICA) technique was applied for feature extraction from the original data and to reduce the correlation between the signals. The correlation coefficient between the independent components and the source data was used as the input parameters of the support vector machine (SVM) classifier. The separation of the independent components was achieved by MATLAB simulation. The misdiagnosis rate was 5% for 40 sets of test data. A 13-point test platform for noise testing of the loader gearbox was built according to Chinese national standards. Source signals under the normal and fault conditions were analyzed separately by ICA and SVM algorithms. In this case, the misdiagnosis rate was 7.5% for the 40 sets of experimental test data. This proved that the proposed method could effectively realize fault classification and recognition.

scholarly journals Fault Diagnosis of a Hydraulic Pump Based on the CEEMD-STFT Time-Frequency Entropy Method and Multiclass SVM Classifier

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Wanlin Zhao ◽  
Zili Wang ◽  
Jian Ma ◽  
Lianfeng Li
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Entropy Method ◽  
Normal Operation ◽  
Support Vector ◽  
Svm Classifier ◽  
Hydraulic Pump ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Mixing Problem

The fault diagnosis of hydraulic pumps is currently important and significant to ensure the normal operation of the entire hydraulic system. Considering the nonlinear characteristics of hydraulic-pump vibration signals and the mode mixing problem of the original Empirical Mode Decomposition (EMD) method, first, we use the Complete Ensemble EMD (CEEMD) method to decompose the signals. Second, the time-frequency analysis methods, which include the Short-Time Fourier Transform (STFT) and time-frequency entropy calculation, are applied to realize the robust feature extraction. Third, the multiclass Support Vector Machine (SVM) classifier is introduced to automatically classify the fault mode in this paper. An actual hydraulic-pump experiment demonstrates the procedure with a complete feature extraction and accurate mode classification.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
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.

scholarly journals Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianwei Cui ◽  
Mengxiao Shan ◽  
Ruqiang Yan ◽  
Yahui Wu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Energy Difference ◽  
Test System ◽  
Support Vector ◽  
Svm Classifier ◽  
Aero Engine ◽  
Aero Engines ◽  
Optimal Set

This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

scholarly journals A Novel Fast Training Method for SVM and Its Application in Fault Diagnosis of Service Robot

2015 ◽  
Vol 11 (6) ◽  
pp. 4 ◽  
Author(s):  
Xianfeng Yuan ◽  
Mumin Song ◽  
Fengyu Zhou ◽  
Yugang Wang ◽  
Zhumin Chen
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Service Robot ◽  
Support Vector ◽  
Svm Classifier ◽  
Processing Unit ◽  
Training Method ◽  
Fast Training

Support Vector Machines (SVM) is a set of popular machine learning algorithms which have been successfully applied in diverse aspects, but for large training data sets the processing time and computational costs are prohibitive. This paper presents a novel fast training method for SVM, which is applied in the fault diagnosis of service robot. Firstly, sensor data are sampled under different running conditions of the robot and those samples are divided as training sets and testing sets. Secondly, the sampled data are preprocessed and the principal component analysis (PCA) model is established for fault feature extraction. Thirdly, the feature vectors are used to train the SVM classifier, which achieves the fault diagnosis of the robot. To speed up the training process of SVM, on the one hand, sample reduction is done using the proposed support vectors selection (SVS) algorithm, which can ensure good classification accuracy and generalization capability. On the other hand, we take advantage of the excellent parallel computing abilities of Graphics Processing Unit (GPU) to pre-calculate the kernel matrix, which avoids the recalculation during the cross validation process. Experimental results illustrate that the proposed method can significantly reduce the training time without decreasing the classification accuracy.

A rate-of-change-of-current based fault classification technique for thyristor-controlled series-compensated transmission lines

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nishant H. Kothari ◽  
Bhavesh R. Bhalja ◽  
Vivek Pandya ◽  
Pushkar Tripathi
Keyword(s):  
Transmission Lines ◽  
Noise Signal ◽  
Current Transformer ◽  
Rate Of Change ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Technique ◽  
Suggested Technique ◽  
Zero Sequence

Abstract This paper presents a new fault classification technique for Thyristor-Controlled Series-Compensated (TCSC) transmission lines using Support Vector Machine (SVM). The proposed technique is based on the utilization of post-fault magnitude of Rate-of-Change-of-Current (ROCC). Fault classification has been carried out by giving ROCC of three-phases and zero sequence current as inputs to SVM classifier. The performance of SVM as a binary-class, and multi-class classifier has been evaluated for the proposed feature. The validity of the suggested technique has been tested by modeling a TCSC based 400 kV, 300 km long transmission line using PSCAD/EMTDC software package. Based on the above model, a large number of diversified fault cases (41,220 cases) have been generated by varying fault and system parameters. The effect of window length, current transformer (CT) saturation, noise-signal, and sampling frequency have also been studied. It has been found that the proposed technique provides an accuracy of 99.98% for 37,620 test cases. Moreover, the performance of the suggested technique has also been found to be consistent upon evaluating in a 12-bus power system model consisting of a 365 kV, 60 Hz, 300 km long TCSC line. Comparative evaluation of the proposed SVM based technique with other recent techniques clearly indicates its superiority in terms of fault classification accuracy.

scholarly journals Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1137 ◽  
Author(s):  
Haoyuan Sha ◽  
Fei Mei ◽  
Chenyu Zhang ◽  
Yi Pan ◽  
Jianyong Zheng
Keyword(s):  
Support Vector Machine ◽  
Singular Value Decomposition ◽  
Least Squares ◽  
Short Circuit ◽  
Singular Value ◽  
Support Vector ◽  
Voltage Sag ◽  
Svm Classifier ◽  
Identification Algorithm ◽  
Value Decomposition

Voltage sag is one of the most serious problems in power quality. The occurrence of voltage sag will lead to a huge loss in the social economy and have a serious effect on people’s daily life. The identification of sag types is the basis for solving the problem and ensuring the safe grid operation. Therefore, with the measured data uploaded by the sag monitoring system, this paper proposes a sag type identification algorithm based on K-means-Singular Value Decomposition (K-SVD) and Least Squares Support Vector Machine (LS-SVM). Firstly; each phase of the sag sample RMS data is sparsely coded by the K-SVD algorithm and the sparse coding information of each phase data is used as the feature matrix of the sag sample. Then the LS-SVM classifier is used to identify the sag type. This method not only works without any dependence on the sag data feature extraction by artificial ways, but can also judge the short-circuit fault phase, providing more effective information for the repair of grid faults. Finally, based on a comparison with existing methods, the accuracy advantages of the proposed algorithm with be presented.

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