Abstract
In this paper, a pneumatic actuator fault diagnosis method based on GA optimized BP neural network and fuzzy logic is proposed. First of all, the Genetic algorithm is used to optimize the weights of BP neural network, overcoming the shortcoming of neural network including over learning and local optimum. Then the normal actuator model is trained by the GA optimized BP neural network using the health data of actuator. The residual is generated by comparing the output of the BP trained actuator model and the actual actuator, which is used to detect the fault. Finally, fuzzy logic reasoning is used to isolate the fault type of actuator. The simulation results based on DAMADICS valve model and Lublin Sugar Factory failure data indicate that the proposed method can detect and diagnosis fault of actuator fast and accurately.
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Feng, Z., Zhang, X., Yang, H. (2013). Research of Pneumatic Actuator Fault Diagnosis Method Based on GA Optimized BP Neural Network and Fuzzy Logic. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_69
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DOI: https://doi.org/10.1007/978-3-642-39068-5_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39067-8
Online ISBN: 978-3-642-39068-5
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