Study on fault diagnosis method for the tail rope of a hoisting system based on machine vision

Abstract

Healthy operation of the tail rope is crucial to the stable and safe operation of a friction hoisting system. Failure of the tail rope will threaten the property and personnel. In this study, a fault diagnosis algorithm based on deep learning is proposed for the tail rope. Specifically, we add a spatial attention mechanism in the feature extraction stage to assign different weights to different regions in images. This way “guides” the model to focus on more important regions. A class-balance cross-entropy loss is introduced to alleviate the imbalanced data distribution in the actual conditions for enhancing the robustness of the algorithm and its transferability in practical applications. Experimental studies are conducted to validate the algorithm. The accuracy of the algorithm on the conducted dataset is 99.4819%. The accuracy of the provided algorithm is increased by 10% and 7% compared with those of the hand-crafted features, namely, scale-invariant feature transform with support vector machine and random forest, respectively. Results show that the proposed algorithm can meet the requirements of high accuracy and generalization in practical engineering applications.