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Appl. Sci. 2018, 8(8), 1346; https://doi.org/10.3390/app8081346

Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network

1
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
2
Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, Xuzhou 221116, China
3
School of Mechanical and Electrical Engineering, Jiangsu Normal University, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Received: 17 July 2018 / Accepted: 8 August 2018 / Published: 10 August 2018
(This article belongs to the Section Mechanical Engineering)
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Abstract

With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is adopted to prevent overfitting. Then, a method of image dataset description and establishment that can cover the entire feature space of overhanging BTRs is put forward. Finally, the CNN and two traditional data mining algorithms, namely, k-nearest neighbor (KNN) and an artificial neural network with back propagation (ANN-BP), are adopted to train and test the established dataset, and the influence of hyperparameters on the network diagnostic accuracy is investigated experimentally. The experimental results showed that the CNN could effectively avoid complex steps such as manual feature extraction, that the learning rate and batch-size strongly affected the accuracy and training efficiency, and that the fault diagnosis accuracy of CNN was 100%, which was higher than that of KNN and ANN-BP. Therefore, the proposed CNN with high accuracy, real-time functioning and generalization performance is suitable for application in the health monitoring of hoisting system BTRs. View Full-Text
Keywords: health monitoring; hoisting system; balancing tail ropes; convolutional neural network; image processing; ANN-BP health monitoring; hoisting system; balancing tail ropes; convolutional neural network; image processing; ANN-BP
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zhou, P.; Zhou, G.; Zhu, Z.; Tang, C.; He, Z.; Li, W.; Jiang, F. Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network. Appl. Sci. 2018, 8, 1346.

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