Gear Fault Signal Modeling Study Based on Probability Box Theory

2014 ◽  
Vol 599-601 ◽  
pp. 1225-1228
Author(s):  
An Liu ◽  
Yi Du ◽  
Jia Man Ding
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Failure Modes ◽  
New Method ◽  
Signal Modeling ◽  
Gear Fault ◽  
Rich Information ◽  
The Rich ◽  
Almost All

Gears typical failure modes and fault diagnosis methods were summarized, and their characteristics and deficiency were contrasted. As almost all method need feature extraction before information fusion, the rich information in original signals were lost in this process. Another difficult problems of information fusion is the the space-time registration. The probability box theory can be a new method to solve the above two problems. The gears fault signal modeling method based on probability box theory were then proposed. Finally the prospects and study directions of this method’s applications in gear box fault diagnosis were proposed.

A new method of gear fault diagnosis in strong noise based on multi-sensor information fusion

2014 ◽  
Vol 22 (6) ◽  
pp. 1504-1515 ◽  
Author(s):  
Gang Cheng ◽  
Xi-hui Chen ◽  
Xian-lei Shan ◽  
Hou-guang Liu ◽  
Chang-fei Zhou
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
New Method ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Sensor Information

A New Method of Nonlinear Feature Extraction for Multi-Fault Diagnosis of Rotor Systems

2010 ◽  
Vol 41 (10) ◽  
pp. 29-37 ◽  
Author(s):  
Zhixiong Li ◽  
Xinping Yan ◽  
Chengqing Yuan ◽  
Jiangbin Zhao ◽  
Zhongxiao Peng
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
New Method ◽  
Rotor Systems ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

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 Based on Multi-Sensor Information Fusion with SVM

2010 ◽  
Vol 34-35 ◽  
pp. 995-999 ◽  
Author(s):  
Xue Jun Li ◽  
D.L. Yang ◽  
Ling Li Jiang
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Binary Tree ◽  
Recognition Rate ◽  
Support Vector ◽  
Bearing Fault ◽  
Penalty Factor ◽  
Sensor Information

This paper proposed a fault diagnosis based on multi-sensor information fusion for rolling bearing. This method used the energy value of multiple sensors is used as feature vector and a binary tree support vector machine (Binary Tree Support Vector Machine, BT-SVM) is used for pattern recognition and fault diagnosis. By analyzing the training samples, penalty factor and the kernel function parameters have effects on the recognition rate of bearing fault, then a approximate method to determine optimum value are proposed, Compared with the traditional single sensor by using the components energy of EMD as feature, the results show that the proposed method in this paper significantly reduce feature extraction time, and improve diagnostic accuracy, which is up to99.82%. This method is simple, effective and fast in feature extraction and meets the bearing diagnosis requirement of real-time fault diagnosis.

Gear Fault Diagnosis Using Synchro-Squeezing Transform Based Feature Analysis

2013 ◽  
Vol 569-570 ◽  
pp. 449-456 ◽  
Author(s):  
Budhaditya Hazra ◽  
Sriram Narasimhan
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Mathematical Structure ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
The Rich ◽  
The Individual ◽  
Non Stationary Signal

Synchro-squeezing transform has recently emerged as a powerful signal processing tool in non-stationary signal processing. Premised upon the concept of time-frequency (TF) reassignment, its basic objective is to provide a sharper representation of signals in the TF plane and extract the individual components of a non-stationary multi-component signal, akin to empirical mode decomposition (EMD). The rich mathematical structure based on continuous wavelet transform (CWT) makes synchro-squeezing powerful for gear fault diagnosis, as faulty gear signal is frequently constituted out of multiple amplitude-modulated and frequency-modulated signals embedded in noise. This work utilizes the decomposing power of synchro-squeezing transform to extract the IMFs from a gear signal followed by the application of standard gearbox condition indicators which promises greater prognostic power than that can be achieved by applying condition indictors directly to the inherently complex gear signals. The efficacy and the robustness of the algorithm are demonstrated with the aid of practical experimental data obtained from a helicopter gear box.

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