Intelligent Fault Diagnosis of Broken Wires for Steel Wire Ropes Based on Generative Adversarial Nets

Abstract

The quantitative identification of broken wires is of great significance to maintain the safety of mechanical systems, such as steel wire ropes. However, in order to achieve high accuracy recognition results, a large number of fault samples are necessary, which is difficult to achieve in practical industrial detection. In this paper, a novel quantitative identification approach, based on generative adversarial nets (GANs) and a convolutional neural network (CNN), is proposed to solve the broken wire recognition problem in situations where real inspections have generated only a small sample of broken wires for analysis. One-dimensional original signals of broken wires are transformed into two-dimensional time-frequency images by continuous wavelet transform (CWT). Next, these time-frequency images are used for quantitative identification of various defects by combing GANs and CNN with limited samples. The main innovation of this paper is that the identification accuracy of broken wires can be improved by generating fault samples through GANs. The experimental results demonstrate that the proposed method achieves better recognition rates for broken wires compared with the existing detection methods.