Abstract
Crane as large mechanical equipment, plays an irreplaceable role in industrial production. Crane fault diagnosis technology, which improves safety and reliability of crane operation, becomes extremely important. The BP neural network has been utilized to study crane state monitoring and fault diagnosis. A BP neural network model was established to monitor tower crane running status online, and simulation experiments were made on the stability of the model. Results show that the BP neural network model can accurately monitor tower crane running status, give effectively fault prediction, and improve security and reliability of tower crane operation.
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Acknowledgments
This work is supported by the Natural Science Foundation of Henan Province under Grant No. 132102210091 and No. 142102210077 and No.142102210105.
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Yu, JL., Zhou, RF., Miao, MX., Huang, HQ. (2015). An Application of Artificial Neural Networks in Crane Operation Status Monitoring. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46463-2_24
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DOI: https://doi.org/10.1007/978-3-662-46463-2_24
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