scholarly journals Bearing Fault Classification Based on Conditional Random Field

2013 ◽  
Vol 20 (4) ◽  
pp. 591-600 ◽  
Author(s):  
Guofeng Wang ◽  
Xiaoliang Feng ◽  
Chang Liu
Keyword(s):  
Random Field ◽  
Graphical Model ◽  
Conditional Random Field ◽  
Wavelet Packet ◽  
Rolling Element Bearing ◽  
Fault Classification ◽  
Mechanical Equipment ◽  
Conditional Probability Distribution ◽  
Outer Race ◽  
Feature Vectors

Condition monitoring of rolling element bearing is paramount for predicting the lifetime and performing effective maintenance of the mechanical equipment. To overcome the drawbacks of the hidden Markov model (HMM) and improve the diagnosis accuracy, conditional random field (CRF) model based classifier is proposed. In this model, the feature vectors sequences and the fault categories are linked by an undirected graphical model in which their relationship is represented by a global conditional probability distribution. In comparison with the HMM, the main advantage of the CRF model is that it can depict the temporal dynamic information between the observation sequences and state sequences without assuming the independence of the input feature vectors. Therefore, the interrelationship between the adjacent observation vectors can also be depicted and integrated into the model, which makes the classifier more robust and accurate than the HMM. To evaluate the effectiveness of the proposed method, four kinds of bearing vibration signals which correspond to normal, inner race pit, outer race pit and roller pit respectively are collected from the test rig. And the CRF and HMM models are built respectively to perform fault classification by taking the sub band energy features of wavelet packet decomposition (WPD) as the observation sequences. Moreover, K-fold cross validation method is adopted to improve the evaluation accuracy of the classifier. The analysis and comparison under different fold times show that the accuracy rate of classification using the CRF model is higher than the HMM. This method brings some new lights on the accurate classification of the bearing faults.

scholarly journals Multi-range Conditional Random Field for Classifying Railway Electrification System Objects

2016 ◽  
Author(s):  
Jaewook Jung ◽  
Leihan Chen ◽  
Gunho Sohn ◽  
Chao Luo ◽  
Jong-Un Won
Keyword(s):  
Random Field ◽  
Long Range ◽  
Short Range ◽  
Graphical Model ◽  
Conditional Random Field ◽  
Spatial Relations ◽  
Stochastic Gradient Descent ◽  
Occurrence Rate ◽  
Support Vector ◽  
Object Classes

Railway has been used as one of the most crucial means of transportation in public mobility and economic development. For efficiently operating railways, the electrification system in railway infrastructure, which supplies electric power to trains, is essential facilities for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS) data into ten target classes representing overhead wires, movable brackets and poles, which are recognized key objects in the electrification system. In general, the layout of railway electrification system shows a strong regularity of spatial relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF), which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds is converted into a set of line segments where the labeling process is applied. Support Vector Machine (SVM) is used as a local classifier considering only node features for producing the unary potentials of CRF model. As the short-range pairwise contextual term, Potts model is applied to enforce a local smoothness in short-range graph. While, long-range pairwise potentials are designed to enhance spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by Naïve Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables which represent co-occurrence rate of spatial relations in horizontal and vertical directions. The likelihood function is formulated by multivariate Gaussian distributions. In the proposed multi-range CRF model, the weight parameters to balance four sub-terms are estimated by applying the Stochastic Gradient Descent (SGD). The results show that the proposed multi-range CRF can effectively classify detailed railway elements, representing the average recall of 97.66% and the average precision of 97.07% for all classes.

scholarly journals Conditional random field based image and video content analysis

2021 ◽  
Author(s):  
Xiaofeng Wang
Keyword(s):  
Content Analysis ◽  
Random Field ◽  
Graphical Model ◽  
Research Effort ◽  
Conditional Random Field ◽  
Semantic Gap ◽  
Video Content ◽  
Video Content Analysis ◽  
Image Labeling ◽  
New Feature

Image and video content analysis is an interesting, meaningful and challenging topic. In recent years much of the research effort in the multimedia field focuses on indexing and retrieval. Semantic gap between low-level features and high-level content is a bottleneck in most systems. To bridge the semantic gap, new content analysis models need to be developed. In this thesis, algorithms based on a relatively new graphical model, called the conditional random field (CRF) model, are developed for two closely-related problems in content analysis: image labeling and video content analysis. The CRF model can represent spatial interactions in image labeling and temporal interactions in video content analysis. New feature functions are designed to better represent the feature distributions. The mixture feature functions are used in image labeling for databases with nature images, and the independent component analysis (ICA) mixture function is applied in sports video content analysis. The spatial dependence of image parts and the temporal dependence of video frames can be explored by the CRF model more effectively using new feature functions. For image labeling with large databases, the content-based image retrieval method is combined with the CRF image labeling model successfully.

scholarly journals Exploiting graph neural network with one-shot learning for fault diagnosis of mechanical equipment

2021 ◽  
Vol 1207 (1) ◽  
pp. 012022
Author(s):  
Shuai Yang ◽  
Xu Chen ◽  
Yun Bai
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Fault Classification ◽  
Mechanical Equipment ◽  
Data Set ◽  
Feature Vectors ◽  
Public Data ◽  
Rv Reducer

Abstract For the classification of mechanical fault diagnosis, a graph neural network (GNN) method with one-shot learning is proposed. Convolutional Neural Network (CNN) is used to extract the feature vectors and One-Hot coding from images of Fault diagnosis of mechanical equipment. Inputting feature vectors and One-Hot coding into GNN, according to the Adjacency Matrix between vertices in the Graph, and is used for classification and inference. The method with one-shot learning is used for fault diagnosis classification. Through the fault classification for the industrial robot RV reducer and public data set CWRU pictures, the effectiveness of the method is verified. Five categories are used for fault diagnosis and classification in RV Reducer of the industrial robots. 80 categories are used in the public data set CWRU, and 55 categories are used as the training set. GNN is employed to spread the label information from the supervised sample of the unlabeled query data. The large-scale dataset can then be used as baseline classes to learn transferable knowledge for classifying novelties with one-shot samples. The one-shot learning with graph neural network GNN significantly improves the classification accuracy. The results show that the proposed method is superior to other similar methods and has a substantial potential for improvement in Fault diagnosis of mechanical equipment.

scholarly journals Dynamic Modeling and Analysis of Rolling Bearing with Compound Fault on Raceway and Rolling Element

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xin Zhang ◽  
Changfeng Yan ◽  
Yaofeng Liu ◽  
Pengfei Yan ◽  
Yubo Wang ◽  
...  
Keyword(s):  
Rolling Bearing ◽  
Vibration Response ◽  
Rolling Element Bearing ◽  
Mechanical Equipment ◽  
Vibration Signals ◽  
Modeling And Analysis ◽  
Outer Race ◽  
Rolling Element ◽  
Element Bearing ◽  
Localized Faults

Rolling element bearing is a very important part of mechanical equipment and widely used in rotating machinery. Rolling element bearings could appear localized defects during the working condition, which would cause the complex vibration response of bearings. Considering the shaft and bearing pedestal, a 4 degree-of-freedom (DOF) dynamic model of rolling bearing with compound localized fault is established based on time-varying displacement, and the vibration characteristics of rolling bearing with localized faults under different conditions are investigated. The established model is verified by the experimental vibration signals in time domain and frequency domain. The results show that the vibration response of compound fault is the result of the coupling action of a single fault of rolling element and outer race. The influences of compound fault on the vibration signals of the bearing were analyzed under three conditions. With the increasing of radial load, defect size, and rotation speed, the vibration amplitude of bearing would increase correspondently, which would accelerate the failure rate of bearing and reduce the service life of bearing. This model is helpful to analyze the vibration response of the rolling element bearing with single or compound fault.

scholarly journals Conditional random field based image and video content analysis

2021 ◽  
Author(s):  
Xiaofeng Wang
Keyword(s):  
Content Analysis ◽  
Random Field ◽  
Graphical Model ◽  
Research Effort ◽  
Conditional Random Field ◽  
Semantic Gap ◽  
Video Content ◽  
Video Content Analysis ◽  
Image Labeling ◽  
New Feature

Image and video content analysis is an interesting, meaningful and challenging topic. In recent years much of the research effort in the multimedia field focuses on indexing and retrieval. Semantic gap between low-level features and high-level content is a bottleneck in most systems. To bridge the semantic gap, new content analysis models need to be developed. In this thesis, algorithms based on a relatively new graphical model, called the conditional random field (CRF) model, are developed for two closely-related problems in content analysis: image labeling and video content analysis. The CRF model can represent spatial interactions in image labeling and temporal interactions in video content analysis. New feature functions are designed to better represent the feature distributions. The mixture feature functions are used in image labeling for databases with nature images, and the independent component analysis (ICA) mixture function is applied in sports video content analysis. The spatial dependence of image parts and the temporal dependence of video frames can be explored by the CRF model more effectively using new feature functions. For image labeling with large databases, the content-based image retrieval method is combined with the CRF image labeling model successfully.

“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 251
Author(s):  
Yan Yan ◽  
Faguo Zhou ◽  
Yifan Ge ◽  
Cheng Liu ◽  
Jingwu Feng
Keyword(s):  
Random Field ◽  
Mobile Applications ◽  
Conditional Random Field ◽  
User Reviews ◽  
Visualization System ◽  
Target Extraction

With the spread of mobile applications and online interactive platforms, the number of user reviews are increasing explosively and becoming one of the most important ways for users to voice opinions. Opinion target extraction and opinion word extraction are two key procedures used to determine the helpfulness of reviews. In this paper, we implement a system to extract “opinion target:opinion word” pairs based on the Conditional Random Field (CRF). Firstly, we used the CRF model to extract opinion targets and opinion words, then combined these into pairs in order. In addition, Node.js was used to build a visualization system to display “opinion target:opinion word” pairs. In order to verify the effectiveness of the system, experiments were conducted on the Laptop and Restaurant datasets of SemEval-2014-task4, and the accuracy of the F value extracted by the model reached 86% and 90%, respectively. All the code and datasets for this experiment are available on GitHub.

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