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Fault Diagnosis of Hoist Braking System Based on Improved Particle Swarm Optimization Algorithm

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Proceedings of 2017 Chinese Intelligent Systems Conference (CISC 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 459))

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Abstract

Reliability of the mine hoist braking system is directly related to the safety of staff in the pit. For the sake of improving the accuracy of the fault diagnosis of the hoist braking system, aradial basis function (RBF) neural network diagnostic method based on improved particle swarm optimization (PSO) algorithm is proposed. Then, the hoist braking system fault diagnosis model is established, which uses some kinds of braking system fault characteristic parameters as input variables and adopts several kinds of main fault types as output ones. In view of the strong global convergence of the genetic algorithm (GA), the idea of crossover and mutation is introduced into PSO and the paper employs to optimize the parameters of hidden layer of RBF neural network. The simulation results show that the improved diagnosis strategy improves fault diagnostic speed and precision of the hoist braking system.

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Acknowledgements

Support by National Key Research and Development Program (2016YFC0600906).

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, China

    Lei Yao, Fu Zhong Wang & Su Min Han

Authors
  1. Lei Yao
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  2. Fu Zhong Wang
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  3. Su Min Han
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Corresponding author

Correspondence to Fu Zhong Wang .

Editor information

Editors and Affiliations

  1. Beihang University , Beijing, China

    Yingmin Jia

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  3. University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

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Yao, L., Wang, F.Z., Han, S.M. (2018). Fault Diagnosis of Hoist Braking System Based on Improved Particle Swarm Optimization Algorithm. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-6496-8_4

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  • DOI: https://doi.org/10.1007/978-981-10-6496-8_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6495-1

  • Online ISBN: 978-981-10-6496-8

  • eBook Packages: EngineeringEngineering (R0)

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