scholarly journals Compound Fault Diagnosis of Rolling Bearing Based on ALIF-KELM

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Ma ◽  
Shitong Liang ◽  
Zhengyu Du ◽  
Ming Chen
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Training Set ◽  
Sample Data ◽  
Diagnosis Method ◽  
Main Fault ◽  
Bp Model ◽  
Fault Information

Aiming at the shortcomings of difficult classification of rolling bearing compound faults and low recognition accuracy, a composite fault diagnosis method of rolling bearing combined with ALIF and KELM is proposed. First, the basic concepts of ALIF and KELM are introduced, and then ALIF is used to decompose the sample data of vibration signals of different bearing states so that each sample can get several IMFs, select the top K IMFs containing the main fault information from each sample, calculate the energy feature and sample entropy of each IMF, and construct a fault feature vector with a dimension of 2K. Finally, the feature vectors of the training set and the test set are input into the KELM model for fault classification. Experimental results show that, compared with EMD-KELM model, ALIF-ELM model, ALIF-BP model, and IFD-KELM model, the rolling bearing composite fault diagnosis method based on the ALIF-KELM model has higher classification accuracy.

scholarly journals Fine-Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Guodong Sun ◽  
Yuan Gao ◽  
Kai Lin ◽  
Ye Hu
Keyword(s):  
Fault Diagnosis ◽  
Dictionary Learning ◽  
Nonnegative Matrix ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Time Frequency ◽  
Fine Grained ◽  
Diagnosis Method

To accurately diagnose fine-grained fault of rolling bearing, this paper proposed a new fault diagnosis method combining multisynchrosqueezing transform (MSST) and sparse feature coding based on dictionary learning (SFC-DL). Firstly, the high-resolution time-frequency images of raw vibration signals, including different kinds of fine-grained faults of rolling bearing, were constructed by MSST. Then, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC), and the trained basis dictionary was employed to extract features from time-frequency matrixes by using nonnegative linear equations. Finally, a linear support vector machine (LSVM) was trained with features of training samples, and the trained LSVM was employed to diagnosis the fault classification of test samples. Compared with state-of-the-art fault diagnosis methods, the proposed method, which was tested on the bearing dataset from Case Western Reserve University (CWRU), achieved the fine-grained classification of 10 mixed fault states. Meanwhile, the proposed method was applied on the dataset from the Machinery Failure Prevention Technology (MFPT) Society and realized the classification of 3 fault states under different working conditions. These results indicate that the proposed method has great robustness and could better meet the needs of practical engineering.

scholarly journals A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1774
Author(s):  
Jun He ◽  
Ming Ouyang ◽  
Chen Yong ◽  
Danfeng Chen ◽  
Jing Guo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Standard Deviation ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Intelligent Fault Diagnosis ◽  
Softmax Classifier ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Testing Accuracy ◽  
Intelligent Methods

Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network

2020 ◽  
Vol 16 (02) ◽  
pp. 47
Author(s):  
Liqun Hou ◽  
Zijing Li
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Operating Conditions ◽  
Fault Classification ◽  
Rolling Ball ◽  
Diagnosis Method

Rolling bearing plays an important role in rotary machines and industrial processes. Effective fault diagnosis technology for rolling bearing directly affects the life and operator safety of the devices. In this paper, a fault diagnosis method based on tunable-Q wavelet transform (TQWT) and convolutional neural network (CNN) is proposed to reduce the influence of noise on bearing vibration signal and the dependence on the experience of traditional diagnosis methods. TQWT is used to decompose and denoise the vibration signal, while the CNN is adopted to extract fault features and carry out fault classification. Seven motor operating conditions—normal, drive end rolling ball failure (DE-B), drive end inner raceway failure (DE-IR), drive end outer raceway failure (DE-OR), fan end rolling ball failure (FE-B), fan end inner raceway fault (FE-IR) and fan end outer raceway fault (FE-OR)—are used to evaluate the proposed approach. The experimental results indicate that the fault diagnosis accuracy of the proposed method reaches 99.8%.

scholarly journals A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning

2020 ◽  
Vol 10 (5) ◽  
pp. 1768
Author(s):  
Juanli Li ◽  
Shuo Jiang ◽  
Menghui Li ◽  
Jiacheng Xie
Keyword(s):  
Fault Diagnosis ◽  
Decision Tree ◽  
Fault Classification ◽  
Brake System ◽  
Disc Brake ◽  
Decision Tree Algorithm ◽  
Sample Data ◽  
C4.5 Decision Tree ◽  
Diagnosis Method ◽  
Mine Hoist

The performance of the brake system is directly related to the safety and reliability of the mine hoist operation. Mining the useful fault information in the operation of a mine hoist brake system, analyzing the abnormal parts and causes of the equipment, and making accurate early prediction and diagnosis of hidden faults are of great significance to ensure the safe and stable operation of a mine hoist. This study presents a fault diagnosis method for hoist disc brake system based on machine learning. First, the monitoring system collects the information of the hoist brake system, extracts the fault features, and pretreats it by SPSS (Statistical Product and Service Solutions). This work provides data support for fault classification. Then, due to the complex structure of the hoist brake system, the relationship between the fault factors often has a significant impact on the fault. Considering the correlation between the fault samples and the attributes of each sample data, the C4.5 decision tree algorithm is improved by adding Kendall concordance coefficient, and the improved algorithm is used to train the sample data to get the decision tree classification model. Finally, the fault sample of the hoist brake system is trained to get the algorithm model, and then the fault diagnosis rules are generated. The state of the brake system is judged by classifying the data. Experiments show that the improved C4.5 decision tree algorithm takes the relativity of conditional attributes into account, has a higher diagnostic accuracy when processing more data, and has concise and clear fault classification rules, which can meet the needs of hoist fault diagnosis.

scholarly journals Compound Fault Diagnosis of Gearbox Based on RLMD and SSA-PNN

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shitong Liang ◽  
Jie Ma
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Search Algorithm ◽  
Probabilistic Neural Network ◽  
Permutation Entropy ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Local Mean ◽  
Fault Information

In order to solve the difficulty in the classification of gearbox compound faults, a gearbox fault diagnosis method based on the sparrow search algorithm (SSA) improved probabilistic neural network (PNN) is proposed. Firstly, the gearbox fault signal is decomposed into a series of product functions (PFs) by robust local mean decomposition (RLMD). Then, the permutation entropy of PFs, which contains much fault information, is calculated to construct the feature vector and input it into the SSA-PNN model. The experimental results show that compared with the traditional fault diagnosis methods based on EMD-BP and EEMD-PNN, the gearbox fault diagnosis method based on RLMD and SSA-PNN has higher diagnosis accuracy.

Fault diagnosis of rolling bearings based on multi-scale deep subdomain adaptation network

2022 ◽  
pp. 1-11
Author(s):  
Qin Zhou ◽  
Zuqiang Su ◽  
Lanhui Liu ◽  
Xiaolin Hu ◽  
Jianhang Yu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Receptive Fields ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Fault Features ◽  
Diagnosis Method ◽  
Fault Information

This study presents a fault diagnosis method for rolling bearing based on multi-scale deep subdomain adaptation network (MSDSAN). The proposed MSDSAN, as improvement of deep subdomain adaptation network (DSAN), is an unsupervised transfer learning method. MSDSAN reduces the subdomain distribution discrepancy between domains rather than marginal distribution discrepancy, and so better domain invariant fault features are derived to avoid misalignment between domains. Aiming at avoiding fault information loss by fixed receptive fields feature extraction, selective kernel convolution module is introduced into feature extraction of MSDSAN, by which multiple receptive fields are applied to ensure an optimal receptive field for each working condition. Moreover, contribution rates are adaptively assigned to all receptive fields, and the disturbing information extracted by inappropriate receptive fields is further eliminated. As a result, more comprehensive and effective fault information is derived for bearing fault diagnosis. Fault diagnosis experiment of bearings is performed to verify the superiority of the proposed method, and the experimental results demonstrate that MSDSAN achieves better transfer effects and higher accuracy than SOTA methods under varying working conditions.

scholarly journals Fault Diagnosis of Rolling Bearing Using Wireless Sensor Networks and Convolutional Neural Network

2020 ◽  
Vol 16 (11) ◽  
pp. 32
Author(s):  
Liqun Hou ◽  
Zijing Li ◽  
Huaisheng Qu
Keyword(s):  
Fault Diagnosis ◽  
Low Cost ◽  
Rolling Bearing ◽  
Production Equipment ◽  
Operating Conditions ◽  
Fault Classification ◽  
Operating Efficiency ◽  
Data Sets ◽  
Vibration Data ◽  
Diagnosis Method

Rolling bearings are widely used in modern production equipment. Effective bearing fault diagnosis method will improve the reliability of the machinery and increase its operating efficiency. In this paper, a novel fault diagnosis method based on WSN and CNN has been proposed to fully utilize the strong fault classification capability of CNN and the inherent merits of WSNs, such as relatively low cost, convenience of installation, and ease of relocation. The feasibility and effectiveness of proposed system are evaluated using the vibration data sets of seven motor operating conditions released by the Case Western Reserve University Bearing Data Center. The experimental results show the fault diagnosis accuracy of the proposed approach can reach 97.6%.

scholarly journals Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Zhou ◽  
Yujie Cheng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Image Recognition ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Kernel Principal Component Analysis ◽  
Two Dimensional ◽  
Rolling Bearings ◽  
Diagnosis Method

Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP) technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT), is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

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