An intelligent fault diagnosis system for newly assembled transmission

Authors:
Wenli Shang;Xiaofeng Zhou;Jie Yuan
Affiliations:
Industry Control Network and Systems Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;Industry Control Network and Systems Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;College of Electronical Engineering, Xinjiang University, Xinjiang 830021, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2014

Citing 7
Cited 0

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique

Expert Systems with Applications: An International Journal
Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference

Expert Systems with Applications: An International Journal
A multidimensional hybrid intelligent method for gear fault diagnosis

Expert Systems with Applications: An International Journal
Application of mother wavelet functions for automatic gear and bearing fault diagnosis

Expert Systems with Applications: An International Journal
A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox

Expert Systems with Applications: An International Journal
Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Newly assembled automobile transmission has its particular failure characteristic, strict quality testing working procedure on the assembly line is important for quality of automobile transmission. In this paper, we introduce a new automatic fault detection method for automobile transmission. A fault diagnosis expert system for newly assembled transmission is presented, related method of knowledge representation, feature extraction and fault classification is given. Order spectrum analysis method is used to analyze vibratory signal of automobile transmission. After initial feature vectors set are obtained, improved genetic search strategy is used to select fault features, so as to reduce the dimension of feature vector set. Selected feature vector sets are inputted into the BP neural network for fault identification and classification of the newly assembled automobile transmission. A large number of data are collected from industrial site and analyzed, proposed algorithm is verified to be effective and exact.