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Fault diagnosis system of bridge crane equipment based on fault tree and Bayesian network

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Abstract

A spreader is an important part of a crane used on wharfs for loading and unloading operations. Because of the spreader’s complex structure, poor operation environment, frequent collision accidents, etc., breakdowns and faults often occur, and the causes for the faults are complex. Based on the historical fault data of the spreaders accumulated during their online service for 13 years, by determining top events and boundary conditions using the induction and deduction methods, a complete spreader fault tree is built with three layers of fault phenomena, fault classification, and fault causes. Then, based on the fault tree, a Bayesian network for the spreader fault diagnosis is constructed by establishing the transformation algorithm from the fault tree to the Bayesian network. The junction tree method is used for accurate inference of spreader faults based on the Bayesian network. Finally, a spreader fault diagnosis system is developed, and a case verification is carried out. The system would be of great help to crane operation engineers in fault diagnosis, and it effectively uses historical fault data to support subsequent maintenance.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant No. 51505286) and the National Key Technologies R&D Program (2015BAF18B02).

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Authors and Affiliations

  1. Institute of Intelligent Manufacturing and Information Engineering, School of Mechanical Engineering, Shanghai Key Lab of Advanced Manufacturing, Shanghai Jiao Tong University, Shanghai, China

    Yu Zheng & Fei Zhao

  2. School of Automation, Southeast University, Nanjing, 210096, Jiangsu, China

    Zheng Wang

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  1. Yu Zheng
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  2. Fei Zhao
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  3. Zheng Wang
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Correspondence to Yu Zheng.

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Zheng, Y., Zhao, F. & Wang, Z. Fault diagnosis system of bridge crane equipment based on fault tree and Bayesian network. Int J Adv Manuf Technol 105, 3605–3618 (2019). https://doi.org/10.1007/s00170-019-03793-0

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