Levenberg-Marquardt neural network for gear fault diagnosis

In this study we are trying with the Levenberg-Marquardt neural network model to the problem of gear fault diagnosis. By using second derivative information, the network convergence speed is promoted and the generalization performance is enhanced. Taking a certain gearbox fault signal acquisition ex...

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Bibliographic Details
Main Authors: Tang Jia-li, Liu Yi-jun, Wu Fang-sheng
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Backpropagation algorithms
Computer science
Educational institutions
Electronic mail
Fault diagnosis
Gear fault diagnosis
Gears
Levenberg-Marquardt Algorithm
Mathematical model
Neural network
Neural networks
Software tools
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Summary:In this study we are trying with the Levenberg-Marquardt neural network model to the problem of gear fault diagnosis. By using second derivative information, the network convergence speed is promoted and the generalization performance is enhanced. Taking a certain gearbox fault signal acquisition experimental system for instance, Matlab software and its neural network toolbox are used to model and simulate. The simulation result shows that Levenberg-Marquardt neural network has a good performance for the common gear fault diagnosis and it can identify various types of faults stably and accurately. Furthermore, compared with conventional BP neural network, the Levenberg-Marquardt neural network reduces training epochs and promotes diagnosis accuracy.
DOI:10.1109/ICNDS.2010.5479613