Rolling bearing fault diagnosis based on IGA-ELM network

PI Jun; MA Sheng; HE Jiacheng; KONG Qingguo; LIN Jiaquan; LIU Guangcai

doi:10.7527/S1000-6893.2018.22025

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2018 , Vol. 39 >Issue 9: 422025 - 422036

DOI: https://doi.org/10.7527/S1000-6893.2018.22025

Material Engineering and Mechanical Manufacturing

Rolling bearing fault diagnosis based on IGA-ELM network

PI Jun ,
MA Sheng ,
HE Jiacheng ,
KONG Qingguo ,
LIN Jiaquan ,
LIU Guangcai

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1. General Aviation College, Civil Aviation University of China, Tianjin 300300, China;
2. College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China;
3. Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China;
4. College of Electronic information and Automation, Civil Aviation University of China, Tianjin 300300, China

Received date: 2018-01-17

Revised date: 2018-03-14

Online published: 2018-06-01

Supported by

Central University Basic Research Business Fee Project Civil Aviation University of China Special Funding (3122017056)

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Abstract

To improve the accuracy of fault diagnosis of aero-engine bearings, a diagnosis model is proposed based on the Optimized Extreme Learning Machine by Improved Genetic Algorithm (IGA-ELM). Crossover operation and mutation operation of the genetic algorithm is improved in this paper to overcome the flaw of the traditional genetic algorithm. The input weight matrix of the connection between the input and hidden layers and biases of hidden neurons of the extreme learning machine is optimized by the improved genetic algorithm. The output matrix of the connection between the hidden and output layers was calculated by using the Moore-Penrose algorithm. Four cases including normal rolling bearing, inner ring fault, out ring fault, and ball fault are diagnosed by using the optimized extreme learning machine. The influence of the number of the hidden layer neurons and the activation function on bearing fault diagnosis of the extreme learning machine is also analyzed. To verify to, as a comparative algorithm A comparison of the traditional genetic algorithm, adaptive genetic algorithm and particle swarm optimization algorithm shows the effectiveness of the improved genetic algorithm in extreme learning machine optimization. Analysis shows that the improved genetic algorithm are better than the other algorithms in terms of convergence speed and convergence error.

Key words： aero-engine; bearing fault diagnosis; extreme learning machine; crossover operation; mutation operation; genetic algorithm

Cite this article

PI Jun , MA Sheng , HE Jiacheng , KONG Qingguo , LIN Jiaquan , LIU Guangcai . Rolling bearing fault diagnosis based on IGA-ELM network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018 , 39(9) : 422025 -422036 . DOI: 10.7527/S1000-6893.2018.22025

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