Fault Diagnosis for Shaft System of Hydropower Unit Based on LS-SVM

2013 ◽  
Vol 325-326 ◽  
pp. 660-664
Author(s):  
Ye Zhou ◽  
Shu Tang ◽  
Luo Ping Pan ◽  
Ping Ping Li
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Least Square ◽  
Classification Model ◽  
Fault Classification ◽  
Support Vector ◽  
Shaft System ◽  
Condition Monitoring System ◽  
Energy Components

In this paper, shaft monitoring data in condition monitoring system of hydropower units was used to build the fault classification model based on the least square support vector machine (LS-SVM). By the wavelet packet signal decomposition for unit vibration signal, setting the signal energy components as the study sample, learning of fault diagnosis classifier was conducted, to achieve the diagnosis of common faults in shaft running of hydropower unit.

Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

2020 ◽  
pp. 002072092094058
Author(s):  
Wuqiang Liu ◽  
Jinxing Shen ◽  
Xiaoqiang Yang
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Model ◽  
Coarse Grained ◽  
Fault Classification ◽  
Support Vector ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

The support vector machine (SVM) does not have a fixed parameter selection method and the manual selection of parameters is difficult to determine the validity, which affects the accuracy of recognition. simultaneously, The existing coarse-grained approach cannot effectively analyze the high-frequency components of time series. In view of the shortcomings of the above method, we put forward a new technique of rolling bearings for fault detection, which combines wavelet packet dispersion entropy (WPDE) and artificial fish swarm algorithm (AFSA) optimize support vector machines (AFSA-SVM). First of all, wavelet packet is devoted to decompose the original vibration signal into components of different frequency bands. Secondly, the dispersion entropy (DE) are calculated for each of the obtained frequency band components to acquire more comprehensive and complete fault information. Afterward, Input feature samples into the SVM model for training, and AFSA is used to optimize the parameters of SVM to obtain the optimal value so as to establish the best classification model. Finally, the prepared test set is input into AFSA-SVM for fault classification. The achievement of bearing detection experiments show that this approach can accurately and quickly identify fault types.

Fault Classification Model of Rotor Based on Support Vector Machine

2011 ◽  
Vol 66-68 ◽  
pp. 1982-1987
Author(s):  
Wei Niu ◽  
Guo Qing Wang ◽  
Zheng Jun Zhai ◽  
Juan Cheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Classification Model ◽  
Fault Classification ◽  
Support Vector ◽  
Vibration Signals ◽  
Good Classification ◽  
Speed Change ◽  
Diagnosis Model

The vibration signals of rotating machinery in operation consist of plenty of information about its running condition, and extraction and identification of fault signals in the process of speed change are necessary for the fault diagnosis of rotating machinery. This paper improves DDAG classification method and proposes a new fault diagnosis model based on support vector machine to solve the problem of restricting the rotating machinery fault intelligent diagnosis due to the lack of fault data samples. The testing results demonstrate that the model has good classification precision and can correctly diagnose faults.

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

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.

Gear Fault Diagnosis Based on Wavelet-Support Vector Machines

2010 ◽  
Vol 33 ◽  
pp. 450-453 ◽  
Author(s):  
Jie Zhao ◽  
Chun Hua Li
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Classification Model ◽  
Fault Classification ◽  
Small Samples ◽  
Support Vector ◽  
Noise Interference ◽  
Vector Machines ◽  
Gear Fault ◽  
Vibration Noise

According to the characteristics of gear vibration noise large and fault diagnosis complex, the paper proposes the method of gear fault classification based on wavelet analysis - Support Vector Machines (SVM). This method effectively eliminates the noise interference of the gear signals. The classification model of gear diagnosis applicable to small samples is established and the result of simulation shows that the model can correctly realize gear fault.

Research on Acoustic Mechanical Fault Diagnosis Method of High Voltage Circuit Breaker Based on Improved EEMD

2014 ◽  
Vol 687-691 ◽  
pp. 1054-1057 ◽  
Author(s):  
Xian Ping Zhao ◽  
Zhi Wan Cheng ◽  
Xiang Yu Tan ◽  
Wei Hua Niu
Keyword(s):  
Fault Diagnosis ◽  
High Voltage ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Fault Classification ◽  
Support Vector ◽  
Circuit Breakers ◽  
High Voltage Circuit Breaker ◽  
Fast Independent Component Analysis ◽  
Diagnosis Method

High voltage circuit breaker is one of the most significant devices and its health status will impact security of the power system. In this paper, the method of high voltage circuit breakers mechanical fault diagnosis is discussed, fault diagnosis method based on vibration signal is proposed. Firstly, the collected acoustic signals are proceed by blind source separation processing through fast independent component analysis. Then, the acoustic signal feature vector is extracted by improved ensemble empirical mode decomposition (EEMD) and the residual signal is filtered by fractional differential. Finally, the feature vectors are input into support vector machine (SVM) for fault diagnosis. Experiment shows that the proposed method can get more precise fault classification to high voltage circuit breakers.

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

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.

scholarly journals Application of Feature Fusion Using Coaxial Vibration Signal for Diagnosis of Rolling Element Bearings

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jing Jiao ◽  
Jianhai Yue ◽  
Di Pei ◽  
Zhunqing Hu
Keyword(s):  
Fault Diagnosis ◽  
Feature Fusion ◽  
Random Noise ◽  
Vibration Signal ◽  
Fault Classification ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Rolling Element ◽  
Vibration Signal Analysis

The research of rolling element bearings (REBs) fault diagnosis based on single sensor vibration signal analysis is very common. However, the information provided by an individual sensor is very limited, and the robustness of the system is poor. In this paper, a novel fault diagnosis method based on coaxial vibration signal feature fusion (CVSFF) is proposed to fully analyze the multisensor information of the system and build a more reliable diagnostic system. An ensemble empirical mode decomposition (EEMD) method is used to decompose the original vibration signal into a number of intrinsic mode functions (IMFs). Then the autocorrelation analysis is introduced to reduce the random noise remaining in IMFs. After that, the Rényi entropy is calculated as the feature of bearings. Finally, the features of coaxial vibration signal are fused by a multiple-kernel learning support vector machine (MKL-SVM) to classify bearing conditions. In order to verify the effectiveness of the CVSFF method in REB diagnosis, eight data sets from the Case Western Reserve University Bearing Data Center are selected. The fault classification results demonstrate that the proposed approach is a valuable tool for bearing faults detection, and the fused feature from coaxial sensors improves fault classification accuracy for REBs.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
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.

scholarly journals A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2381 ◽  
Author(s):  
Shangjun Ma ◽  
Wei Cai ◽  
Wenkai Liu ◽  
Zhaowei Shang ◽  
Geng Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Learning Ability ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Network ◽  
Memory Space ◽  
Distribution Features

To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results.

scholarly journals Fault diagnosis of gearbox based on ant colony algorithm optimized support vector machine

2021 ◽  
Vol 2137 (1) ◽  
pp. 012068
Author(s):  
Shilin Sun ◽  
Renxiang Lu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Feature Vector ◽  
Vibration Signal ◽  
Ant Colony ◽  
Fault Classification ◽  
Function Parameter ◽  
Support Vector ◽  
Factor C

Abstract The kernel function parameter g and penalty factor c in Support Vector Machine (SVM) will have an important impact on the fault classification and performance of the support vector machine. Based on this, a fault analysis and diagnosis model using ant colony algorithm to optimize support vector machine is proposed to improve the accuracy of gearbox fault diagnosis. First, the collected original vibration signal is decomposed by EEMD to obtain the modal function component IMF, and then the energy entropy of the IMF component is calculated as the feature vector of the original vibration signal. Finally, the feature vector is input to the support vector optimized by the ant colony algorithm identify and classify in the machine, and finally get the diagnosis result. Comparing ACO-SVM with SVM, the experimental results prove that the ACO-SVM model has a higher fault diagnosis rate, stronger optimization ability, and faster convergence speed.

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