scholarly journals Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Shuai ◽  
Changqing Shen ◽  
Zhongkui Zhu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Morphological Feature ◽  
Morphological Analysis ◽  
Vibration Signal ◽  
Health Condition ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Data Set ◽  
Significant Step

Numerous studies on fault diagnosis have been conducted in recent years because the timely and correct detection of machine fault effectively minimizes the damage resulting in the unexpected breakdown of machineries. The mathematical morphological analysis has been performed to denoise raw signal. However, the improper choice of the length of the structure element (SE) will substantially influence the effectiveness of fault feature extraction. Moreover, the classification of fault type is a significant step in intelligent fault diagnosis, and many techniques have already been developed, such as support vector machine (SVM). This study proposes an intelligent fault diagnosis strategy that combines the extraction of morphological feature and support vector regression (SVR) classifier. The vibration signal is first processed using various scales of morphological analysis, where the length of SE is determined adaptively. Thereafter, nine statistical features are extracted from the processed signal. Lastly, an SVR classifier is used to identify the health condition of the machinery. The effectiveness of the proposed scheme is validated using the data set from a bearing test rig. Results show the high accuracy of the proposed method despite the influence of noise.

scholarly journals Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

2016 ◽  
Vol 8 (12) ◽  
pp. 168781401668308 ◽  
Author(s):  
Shuangyuan Wang ◽  
Yixiang Huang ◽  
Liang Gong ◽  
Lin Li ◽  
Chengliang Liu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Sparse Coding ◽  
Extraction Procedure ◽  
Vibration Signal ◽  
Support Vector ◽  
Data Set ◽  
Vibration Signals ◽  
Efficiency And Effectiveness ◽  
Gear Fault

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study

2016 ◽  
Vol 231 (8) ◽  
pp. 1560-1578 ◽  
Author(s):  
Wentao Mao ◽  
Jianliang He ◽  
Yuan Li ◽  
Yunju Yan
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Domain Knowledge ◽  
Extraction Process ◽  
Health Condition ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Training Time ◽  
Learning Machine

Intelligent fault-diagnosis methods using machine learning techniques like support vector machines and artificial neural networks have been widely used to distinguish bearings’ health condition. However, though these methods generally work well, they still have two potential drawbacks when facing massive fault data: (1) the feature extraction process needs prior domain knowledge, and therefore lacks a universal extraction method for various diagnosis issues, and (2) much training time is generally needed by the traditional intelligent diagnosis methods and by the newly presented deep learning methods. In this research, inspired by the feature extraction capability of auto-encoders and the high training speed of extreme learning machines (ELMs), an auto-encoder-ELM-based diagnosis method is proposed for diagnosing faults in bearings to overcome the aforementioned deficiencies. This paper performs a comparative analysis of the proposed method and some state-of-the-art methods, and the experimental results on the rolling element bearings data set show the effectiveness of the proposed method not only with adaptive mining of the discriminative fault characteristic but also at high diagnosis speed.

scholarly journals Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7467
Author(s):  
Shih-Lin Lin
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Image Feature ◽  
Signal Acquisition ◽  
Time Varying ◽  
Intelligent Fault Diagnosis ◽  
Image Block ◽  
Wide Range

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

Intelligent Fault Diagnosis in Power Plant Using Empirical Mode Decomposition, Fuzzy Feature Extraction and Support Vector Machines

2005 ◽  
Vol 293-294 ◽  
pp. 373-382 ◽  
Author(s):  
Qiao Hu ◽  
Zheng Jia He ◽  
Yanyang Zi ◽  
Zhou Suo Zhang ◽  
Yaguo Lei
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Turbo Generator ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Multi Classification

In this paper, a novel intelligent fault diagnosis method based on empirical mode decomposition (EMD), fuzzy feature extraction and support vector machines (SVM) is proposed. The method consists of two stages. In the first stage, intrinsic mode components are obtained with EMD from original signals and converted into fuzzy feature vectors, and then the mechanical fault can be detected. In the second stage, these extracted fuzzy feature vectors are input into the multi-classification SVM to identify the different abnormal cases. The proposed method is applied to the classification of a turbo-generator set under three different operating conditions. Testing results show that the classification accuracy of the proposed model is greatly improved compared with the multi-classification SVM without feature extraction and the multi-classification SVM with extracting the fuzzy feature from wavelet packets, and the faults of steam turbo-generator set can be correctly and rapidly diagnosed using this model.

scholarly journals A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 128
Author(s):  
Chenbo Xi ◽  
Guangyou Yang ◽  
Lang Liu ◽  
Hongyuan Jiang ◽  
Xuehai Chen
Keyword(s):  
Fault Diagnosis ◽  
Single Channel ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Support Vector ◽  
Data Set ◽  
Rotary Machine ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Low Sensitivity

In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

Intelligent fault diagnosis for rotating machinery using L1/2-SF under variable rotational speed

2020 ◽  
pp. 095440702096462
Author(s):  
Jinrui Wang ◽  
Shanshan Ji ◽  
Baokun Han ◽  
Huaiqian Bao
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotational Speed ◽  
Vibration Signal ◽  
Fault Classification ◽  
Mechanical Transmission ◽  
Intelligent Fault Diagnosis ◽  
Planetary Gearbox ◽  
Non Stationary Signal ◽  
Regularization Strategy

Sparse filtering (SF), as an effective feature extraction technique, has attracted considerable attention in the field of mechanical fault diagnosis. But the generalization ability of SF to handle non-stationary signal under variable rotational speed is still poor. When the rotating parts of mechanical transmission work at a constant speed, the collected vibration signal is strongly correlated with the fault type. However, the mappings will no longer be so simple under the condition of variable rotational speed, which brings a rigorous challenge to intelligent fault diagnosis. To overcome the aforementioned deficiency, a novel L1/2 regularized SF method ( L1/2-SF) is studied in this paper. Specifically, L1/2 regularization strategy is added to the cost function of SF, then the L1/2-SF is directly employed to extract sparse features from the raw vibration data under variable rotational speed condition. In order to understand the sparse feature extraction ability of the L1/2 regularization, a physical explanation of the sparse solution generated by the L1/2 regularization strategy is explored. Next, softmax regression is employed for fault classification connected with the output layer of L1/2-SF. The effectiveness of L1/2-SF method is verified using a planetary gearbox dataset and a bearing dataset, respectively. Experiment results show that L1/2-SF can deal well with the variable rotational speed problem and is superior to other methods.

scholarly journals Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery

2021 ◽  
Vol 11 (3) ◽  
pp. 919
Author(s):  
Jiantao Lu ◽  
Weiwei Qian ◽  
Shunming Li ◽  
Rongqing Cui
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Vibration Signal ◽  
Reconstruction Algorithm ◽  
Nearest Neighbors ◽  
Health Condition ◽  
Rotating Machinery ◽  
Intelligent Fault Diagnosis ◽  
K Nearest Neighbor ◽  
Case Based

Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) commonly required by case-based methods is unable to determine the nearest neighbors for different testing samples adaptively. To solve these problems, a new intelligent fault diagnosis method of rotating machinery is proposed based on enhanced KNN (EKNN), which can take advantage of both parameter-based and case-based methods. First, EKNN is embedded with a dimension-reduction stage, which extracts the discriminative features of samples via sparse filtering (SF). Second, to locate the nearest neighbors for various testing samples adaptively, a case-based reconstruction algorithm is designed to obtain the correlation vectors between training samples and testing samples. Finally, according to the optimized correlation vector of each testing sample, its nearest neighbors can be adaptively selected to obtain its corresponding health condition label. Extensive experiments on vibration signal datasets of bearings are also conducted to verify the effectiveness of the proposed method.

scholarly journals Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6065
Author(s):  
Shih-Lin Lin
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Numerical Control ◽  
Image Feature ◽  
Intelligent Fault Diagnosis ◽  
Data Set ◽  
Diagnosis System ◽  
Wide Range ◽  
Fault Diagnosis System

Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fault diagnosis. Research data are used to compare the performance of the three models through a data set released by the Federal University of Rio de Janeiro (UFRJ). VMD is a non-recursive adaptive signal decomposition method that is suitable for processing the vibration signals of motor equipment under variable working conditions. Applied to bearing fault diagnosis, high-dimensional fault features are extracted. Deep learning shows an absolute advantage in the field of fault diagnosis with its powerful feature extraction capabilities. ResNet101 is used to build a model of motor fault diagnosis. The method of using ResNet101 for image feature learning can extract features for each image block of the image and give full play to the advantages of deep learning to obtain accurate results. Through the three links of signal acquisition, feature extraction, and fault identification and prediction, a mechanical intelligent fault diagnosis system is established to identify the healthy or faulty state of a motor. The experimental results show that this method can accurately identify six common motor faults, and the prediction accuracy rate is 94%. Thus, this work provides a more effective method for motor fault diagnosis that has a wide range of application prospects in fault diagnosis engineering.

scholarly journals Fault diagnosis method of rolling bearings Based on SPA-FE-IFSVM

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096947
Author(s):  
Hui Han ◽  
Lina Hao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fuzzy Entropy ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Rolling Bearings ◽  
Data Set ◽  
Diagnosis Method

Rolling bearings are the most frequently failed components in rotating machinery. Once a failure occurs, the entire system will be shut down or even cause catastrophic consequences. Therefore, a fault detection of rolling bearings is of great significance. Due to the complexity of the mechanical system, the randomness of the vibration signal appears on different scales. Based on the multi-scale fuzzy entropy (FE) analysis of the vibration signal, a rolling bearing fault diagnosis method based on smoothness priors approach (SPA) -FE-IFSVM is proposed. The SPA method was used to adaptively decompose the vibration signal and obtain the trend item and de-trend item of the vibration signal. Then the fuzzy entropy of the trend item and de-trend item was calculated respectively. Meanwhile, aiming at the problem that the support vector machine (SVM) cannot process the data set containing fuzzy messages and was sensitive to noise, the fuzzy support vector machine (FSVM) was introduced and improved, and then the FE as the feature vector was input into the improved fuzzy support vector machine (IFSVM) to identify the failure. The method was applied to the rolling bearing experimental data. The analysis results show that: this method can achieve 100% fault diagnosis accuracy when only two component features are extracted, which can effectively realize the fault diagnosis of rolling bearings.

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