Introduce the Quantitative Identification Method of Rolling Bearing in the Application of Fault Detection

2015 ◽  
Vol 742 ◽  
pp. 147-149
Author(s):  
Li Huo
Keyword(s):  
Rolling Bearing ◽  
Rotating Machinery ◽  
Normal Operation ◽  
Support Vector ◽  
Decision Boundary ◽  
Proposed Model ◽  
Quantitative Identification ◽  
Diagnosis Model ◽  
Bearing Damage ◽  
Vector Description

Rolling bearing is an important part of rotating machinery. Its failure will directly affect the normal operation of the whole machinery. This study proposed an intelligent diagnosis model based on Fuzzy support vector description for the quantitative identification of bearing fault. The proposed model constructs the spherically shaped decision boundary by training the features of normal bearing data, and then calculates the fuzzy monitoring coefficient to identify the bearing damage.

scholarly journals Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 359 ◽  
Author(s):  
Jianghua Ge ◽  
Guibin Yin ◽  
Yaping Wang ◽  
Di Xu ◽  
Fen Wei
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Rolling Bearing ◽  
Support Vector ◽  
Distance Information ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Feature Evaluation ◽  
Diagnosis Method ◽  
Diagnosis Model

To improve the accuracy of rolling-bearing fault diagnosis and solve the problem of incomplete information about the feature-evaluation method of the single-measurement model, this paper combines the advantages of various measurement models and proposes a fault-diagnosis method based on multi-measurement hybrid-feature evaluation. In this study, an original feature set was first obtained through analyzing a collected vibration signal. The feature set included time- and frequency-domain features, and also, based on the empirical-mode decomposition (EMD)-obtained time-frequency domain, energy and Lempel–Ziv complexity features. Second, a feature-evaluation framework of multiplicative hybrid models was constructed based on correlation, distance, information, and other measures. The framework was used to rank features and obtain rank weights. Then the weights were multiplied by the features to obtain a new feature set. Finally, the fault-feature set was used as the input of the category-divergence fault-diagnosis model based on kernel principal component analysis (KPCA), and the fault-diagnosis model was based on a support vector machine (SVM). The clustering effect of different fault categories was more obvious and classification accuracy was improved.

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.

scholarly journals Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaochen Zhang ◽  
Dongxiang Jiang ◽  
Te Han ◽  
Nanfei Wang ◽  
Wenguang Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Imbalanced Data ◽  
Principal Component ◽  
Rotating Machinery ◽  
Support Vector ◽  
Data Set ◽  
Diagnosis Model ◽  
Sample Set

To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

ROLLING BEARING TECHNICAL CONDITION FORECASTING USING VIBRATION ACCELERATION OVERALL LEVEl

2020 ◽  
pp. 12-19
Author(s):  
Yu. V. Sarapulov ◽  
V. A. Sidorov ◽  
A. E. Sushko ◽  
R. A. Khasanov
Keyword(s):  
Practical Importance ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Normal Operation ◽  
Threshold Values ◽  
Vibration Acceleration ◽  
Extensive Experience ◽  
Corporate System ◽  
Vibration Parameters ◽  
Bearing Damage

Traditionally, the assessment of changes in the technical condition of individual components and mechanisms of rotation machines in industry is associated with trends analysis of various vibration parameters. Over the decades of using vibration analysis, we have accumulated extensive experience in faults locating and critically determining, however, it is the assessment of the remaining life that regulates the timing of maintenance and repair activities that is of great practical importance. This article uses the example of a pump unit rolling bearing to consider approaches to predicting the growsup stage of defects based on the analysis of values of vibration acceleration levels. The stages of normal operation and other stages of bearing damage are highlighted, threshold values are calculated and dependences of changes in diagnostic criteria for each stage of the life cycle are constructed. The obtained dependencies show results that are similar in general, but individual in their values, therefore, the accumulation of possible scenarios of events allows creating a knowledge base for predicting the behavior of a mechanical system. The necessary tools for multifactor forecasting were implemented within the SAFE PLANT software platform (LLC SPA DIATECH, Moscow) and are successfully applied at Uralkali PJSC for monitoring the technical condition of all technological equipment and managing MRO processes by integrating the results of diagnostics and forecast assessments into the ORACLE corporate system.

scholarly journals A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yerui Fan ◽  
Chao Zhang ◽  
Yu Xue ◽  
Jianguo Wang ◽  
Fengshou Gu
Keyword(s):  
Support Vector Machine ◽  
High Performance ◽  
Particle Swarm ◽  
Rolling Bearing ◽  
Least Square ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Novel Model

In this paper, a novel model for fault detection of rolling bearing is proposed. It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). The fundamental of multikernel least square support vector machine (MK-LS-SVM) is overviewed to identify a classifier that allows multidimension features from empirical mode decomposition (EMD) to be fused with high generalization property. Then the multidimension parameters of the MK-LS-SVM are configured by the SRPSO for further performance improvement. Finally, the proposed model is evaluated through experiments and comparative studies. The results prove its effectiveness in detecting and classifying bearing faults.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 98
Author(s):  
Haodong Yuan ◽  
Nailong Wu ◽  
Xinyuan Chen ◽  
Yueying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Training Samples ◽  
Diagnosis Method ◽  
Fault Feature Extraction

The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dictionary learned by the training samples, including thedifferent classes, shift invariant sparse feature for the training as well as test samples can be formed through sparse codes and employed as the input of classifier. A support vector machine (SVM) with optimized parameters has been extensively used in intelligent diagnosis of machinery fault. Hence, in this study, a novel fault diagnosis method of rolling bearings using shift invariant sparse feature and optimized SVM is proposed. Firstly, dictionary learning by shift invariant K-SVD algorithm is conducted. Then, shift invariant sparse feature is constructed with the learned over-complete dictionary. Finally, optimized SVM is employed for classification of the shift invariant sparse feature corresponding to different classes, hence, bearing fault diagnosis is achieved. With regard to the optimized SVM, three methods including grid search, generic algorithm (GA), and particle swarm optimization (PSO) are respectively carried out. The experiment results show that the shift invariant sparse feature using shift invariant K-SVD can effectively distinguish the bearing vibration signals corresponding to different running states. Moreover, optimized SVM can significantly improve the diagnosis precision.

Generative Adversarial Network and Transfer Learning Based Fault Detection for Rotating Machinery with Imbalance Data Condition

2021 ◽  
Author(s):  
jun li ◽  
Yongbao Liu ◽  
Qijie Li
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Learning Method ◽  
Intelligent Fault Diagnosis ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Proposed Model ◽  
Diagnosis Method

Abstract Intelligent fault diagnosis achieves tremendous success in machine fault diagnosis because of the outstanding data-driven capability. However, the severe imbalanced dataset in practical scenarios of industrial rotating machinery is still a big challenge for the development of intelligent fault diagnosis method. In this paper, we solve this issue by constructing a novel deep learning model incorporated with a transfer learning method based on the Time-GAN and Efficient-Net models. Firstly, the proposed model so called Time-GAN-TL extends the imbalanced fault diagnosis of rolling bearings by using time series generative adversarial network. Secondly, balanced vibration signals are converted into two-dimensional images for training and classification by implementing the Efficient-Net into the transfer learning method. Finally, the proposed method is validated using two-types of rolling bearing experimental data. The high-precision diagnosis results of the transfer learning experiments and the comparison with other representative fault diagnosis classification methods reveal the efficiency, reliability, and generalization performance of the presented model.

Wavelet Neural Network Based on EKF Algorithm and its Application in Fault Diagnosis for Rotating Machinery

2014 ◽  
Vol 602-605 ◽  
pp. 1741-1744
Author(s):  
Xu Sheng Gan ◽  
Hua Ping Li ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Wavelet Neural Network ◽  
Model Based ◽  
Proposed Model ◽  
Reliable Diagnosis ◽  
Diagnosis Model

It is difficult to realize an accurate and reliable diagnosis in the rotating machinery. To solve this problem, a Wavelet Neural Network (WNN) diagnosis model based on EKF algorithm is proposed. In the model, EKF algorithm is introduced to optimize the parameters of WNN, and then the built WNN model is used to diagnose the faults of the rotating machinery. The experiment shows that, the proposed model has a good diagnosis capability in the field of the rotating machinery.

scholarly journals A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Lei You ◽  
Wenjie Fan ◽  
Zongwen Li ◽  
Ying Liang ◽  
Miao Fang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Fault Classification ◽  
Support Vector ◽  
Local Optimum ◽  
Classification Rate ◽  
Wavelet Energy ◽  
Diagnosis Model

Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time-spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog-leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA-SVM), and SFLA-SVM. The experiment results show that MSFLA-SVM achieves a much higher fault classification rate than BPNN, ACROA-SVM, and SFLA-SVM.

scholarly journals Convolution Diagnosis Model of Centrifugal Pump Based on Fractal Dimension

2021 ◽  
Vol 2095 (1) ◽  
pp. 012061
Author(s):  
Xiaopeng Liu ◽  
Bingxiang Sun ◽  
Jiuchun Jiang ◽  
Weige Zhang ◽  
Chunlu Zhao ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Vibration Signal ◽  
Normal Operation ◽  
Stable Operation ◽  
Centrifugal Pumps ◽  
Mode Decomposition ◽  
Proposed Model ◽  
On Line ◽  
Diagnosis Model ◽  
Operation Status

Abstract Nowadays, more and more centrifugal pumps are used in the industrial domain. Normal operation of centrifugal pumps is also the key to maintain stable operation of production and process. So it is very important to monitor the operation status of centrifugal pumps real time on-line. In order to diagnose the fault of centrifugal pumps, we proposed a Convolutional Neural Network (CNN) fault diagnosis model, based on Fractal Dimension (FD) features. Firstly, we collected the vibration signal of centrifugal pumps and preprocessed them. Then we used the Empirical Mode Decomposition (EMD) to calculate the fractal dimension and the fractal matrix of the signal. Finally, we constructed the CNN model to diagnose and classify the fault of centrifugal pumps. The experiment shows that the accuracy of the proposed model can reach over 90%.

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