Fault Diagnosis of Rolling Bearing Based on Rough Set and Neural Network

2011 ◽  
Vol 58-60 ◽  
pp. 974-977 ◽  
Author(s):  
Jun Rong Yan ◽  
Yong Min ◽  
Xia Cui ◽  
Yan Huang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Rolling Bearing ◽  
Original Data ◽  
Training Data ◽  
Resolving Power ◽  
Self Learning ◽  
Test Result ◽  
The Greedy Algorithm

Artificial neural network was one of the most important methods in intelligent fault diagnosis because it has the performance of nonlinear pattern classification and the capacity of self-learning and self-organization, but it can not judge redundancy and usefulness of information. Rough set can reduce the knowledge of information system and dislodge redundant information. In this paper, fault data of rolling bearing was reduced by the greedy algorithm of rough set. Training data and test data of BP neural network had been reduced by rough set. By comparison of two test result about simply data and original data, it was indicated that resolving power was unchanged and database was simply.

scholarly journals A Multimodel Decision Fusion Method Based on DCNN-IDST for Fault Diagnosis of Rolling Bearing

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Weixiao Xu ◽  
Luyang Jing ◽  
Jiwen Tan ◽  
Lianchen Dou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Evidence Theory ◽  
Rolling Bearing ◽  
Original Data ◽  
Decision Fusion ◽  
Fusion Method ◽  
Rolling Bearings ◽  
Advantages And Disadvantages

Each pattern recognition method has its advantages and disadvantages to diagnose the state of rotating machinery. There are many fault types of rolling bearings with apparent uncertainty. The optimal fusion level is usually challenging to be selected for a specific fault diagnosis task, and extensive human labour and prior knowledge are also highly required during these selections. To solve the above problems, a multimodel decision fusion method based on Deep Convolutional Neural Network and Improved Dempster-Shafer Evidence Theory (DCNN-IDST) is proposed for the inspection of rolling bearing. To solve the defect of the original evidence theory method in the fusion of high-conflict evidence, the fuzzy consistency matrix is introduced. By calculating the factor weight, the reliability and rationality of D-S evidence theory are improved. The DCNN model can learn features from the original data and carry out adaptive feature extraction for multiple sensor information. The features extracted by DCNN adaptively are input into multiple network models for decision fusion. The new method of DCNN-IDST multimodel decision fusion is applied to detect the damage of rolling bearings. To evaluate the effectiveness of the proposed method, both the BP neural network and RBF neural network are used to set up a multigroup comparison test. The result demonstrates that the proposed method can detect the fault of the rolling bearing effectively and achieve the highest diagnosis accuracy among all the tested methods in the experiment.

Fault Diagnosis Based on Rough Set and BP Neural Network (RS-BP) for Gas Turbine Engine

2013 ◽  
Vol 732-733 ◽  
pp. 397-401 ◽  
Author(s):  
Ning Bo Zhao ◽  
Shu Ying Li ◽  
Shuang Yi ◽  
Yun Peng Cao ◽  
Zhi Tao Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Rough Set ◽  
Bp Neural Network ◽  
Rough Set Theory ◽  
Original Data ◽  
Optimal Decision ◽  
Identification Rate ◽  
Diagnosis Model

This paper presents a new fusion diagnosis based on rough set and BP neural network for the fault diagnosis of gas turbine. The frame is designed to fusion fault diagnosis, which is composed by three parts: the rough set data pre-processor, rough set diagnosis model and BP neural network diagnosis model. Aiming at the difficulty in getting adequate fault samples in fault diagnosis, rough set theory is first used to process the original data, establish the decision table and generate rules, which can eliminate the redundant information and build the rough set diagnosis model. After that, according to the optimal decision attribute pre-treated by rough set, BP neural network is designed for fault diagnosis, which can reduce the scale of neural network, improve the identification rate, and improve the efficiency of the whole fusion diagnosis system. Finally, an example of gas turbine generator sets fuel system is taken as a case study to demonstrate the feasibility and validity of the proposed method in this paper.

scholarly journals A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions

2019 ◽  
Vol 9 (8) ◽  
pp. 1603 ◽  
Author(s):  
Ma ◽  
Li ◽  
An
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Speech Recognition ◽  
Convolutional Neural Network ◽  
Loss Function ◽  
Feature Learning ◽  
Rolling Bearing ◽  
Original Data ◽  
Promising Tool ◽  
Fault Features

Intelligent fault diagnosis is a promising tool for processing mechanical big data. It can quickly and efficiently process the collected signals and provide accurate diagnosis results. However, rotating machinery often works under various speed conditions, which makes it difficult to extract fault features. Inspired by speech recognition, the nuisance attribute projection method in speech recognition is introduced into fault diagnosis to solve the problem of feature extraction in variable speed signals. Based on the idea of unsupervised feature learning, the loss function of nuisance attribute projection is added to the loss function of convolutional neural network (CNN) to learn fault features from original data. Health status is classified according to the learned characteristics and projection matrix P. A special designed bearing dataset is employed to verify the effectiveness of the proposed method. The results show that the proposed method has a higher accuracy and a simpler framework, which is superior to the existing methods in bearing fault diagnosis.

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

2021 ◽  
pp. 095440622110329
Author(s):  
Jianqun Zhang ◽  
Qing Zhang ◽  
Xianrong Qin ◽  
Yuantao Sun
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Rolling Bearing ◽  
Training Data ◽  
Variable Load ◽  
K Nearest Neighbor ◽  
Target Domain ◽  
Bearing Faults ◽  
Load Conditions

To identify rolling bearing faults under variable load conditions, a method named DISA-KNN is proposed in this paper, which is based on the strategy of feature extraction-domain adaptation-classification. To be specific, the time-domain and frequency-domain indicators are used for feature extraction. Discriminative and domain invariant subspace alignment (DISA) is used to minimize the data distributions’ discrepancies between the training data (source domain) and testing data (target domain). K-nearest neighbor (KNN) is applied to identify rolling bearing faults. DISA-KNN’s validation is proved by the experimental signal collected under different load conditions. The identification accuracies obtained by the DISA-KNN method are more than 90% on four datasets, including one dataset with 99.5% accuracy. The strength of the proposed method is further highlighted by comparisons with the other 8 methods. These results reveal that the proposed method is promising for the rolling bearing fault diagnosis in real rotating machinery.

Fault Diagnosis of Rolling Bearings Based on LLE_KFDA

2009 ◽  
Vol 626-627 ◽  
pp. 529-534 ◽  
Author(s):  
Guang Bin Wang ◽  
Y.I. Liu ◽  
X.Q. Zhao
Keyword(s):  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Rolling Bearing ◽  
Original Data ◽  
Locally Linear Embedding ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis ◽  
Nonlinear Dimension ◽  
Dimension Space

Locally linear embedding (LLE) algorithm is an unsupervised technique recently proposed for nonlinear dimension reduction. In this paper,LLE manifold learning algorithm is introduced into the field of equipment fault diagnosis firstly, a method of the fault diagnosis based on LLE_KFDA is proposed. By LLE algorithm, original sample data is directly mapped to its’ intrinsical dimension space,which data still keep primary nonlinear form. then via kernel fisher discriminant analysis(KFDA), the characteristics data in intrinsical dimension space are mapped into knernel high-dimensional linear space,and then different fault data are discriminated based on a criterion of between-class and insid-class deviatione ratio maximum. LLE_KFDA algorithm is based on original data, avoided from fall of pattern recognition ability which caused by inappropriate or blind choice of the feature parameters in the traditional fault diagnosis method.The experiment to fault diagnosis of rolling bearing shows this method can effectively identify the equipment fault pattern, diagnostic result is good.

Sign in / Sign up

Export Citation Format

Share Document