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.

As a load-bearing tool, steel wire rope plays an important role in industrial production. Therefore, diagnosing the fracture and damage of steel wire ropes is of great significance for ensuring their safe operation. However, the detection and identification of wire rope breakage damage mainly focus on identifying external damage characteristics, while research on inspecting internal breakage damage is still relatively limited. To address the challenge, an intelligent detecting method is proposed in this paper for diagnosing internal wire breakage damage, and it introduces residual modules to enhance the network’s feature extraction ability. Firstly, time–frequency analysis techniques are used to convert the extracted one-dimensional magnetic flux leakage (MFL) signal into a two-dimensional time–frequency map. Secondly, the focus of this article is on constructing a residual network to identify the internal damage accurately with the features of the time–frequency map of the MFL signal being automatically extracted. Finally, the effectiveness of the proposed method in identifying broken wires is verified through comparative experiments on detecting broken wires in steel wire ropes. Three common recognition methods, the backpropagation (BP) neural network, the support vector machine (SVM), and the convolutional neural network (CNN), are used as comparisons. The experimental results show that the residual network recognition method can effectively identify internal and external wire breakage faults in steel wire ropes, which is of great significance for achieving quantitative detection of steel wire ropes.

The identification of the film on cotton is of great significance for the improvement of cotton quality. Most of the existing technologies are dedicated to removing colored foreign fibers from cotton using photoelectric sorting methods. However, the current technologies are difficult to identify colorless transparent film, which becomes an obstacle for the harvest of high-quality cotton. In this paper, an intelligent identification method is proposed to identify the colorless and transparent film on cotton, based on short-wave near-infrared hyperspectral imaging and convolutional neural network (CNN). The algorithm includes black-and-white correction of hyperspectral images, hyperspectral data dimensionality reduction, CNN model training and testing. The key technology is that the features of the hyperspectral image data are degraded by the principal component analysis (PCA) to reduce the amount of computing time. The main innovation is that the colorless and transparent film on cotton can be accurately identified through a CNN with the performance of automatic feature extraction. The experimental results show that the proposed method can greatly improve the identification precision, compared with the traditional methods. After the simulation experiment, the method proposed in this paper has a recognition rate of 98.5% for film. After field testing, the selection rate of film is as high as 96.5%, which meets the actual production needs.

Abrasive filament brushes have been widely used in surface processes for a wide range of applications, including blending, edge-radiusing, and polishing. However, the associated brush mechanics of material removal is still not clear. In order to analyze the brush grinding of aluminium alloy, this paper constructed a kinematic model of a single filament, simulated the scratch process of a single abrasive grain, and investigated the brush force and material removal based on the finite element approach. The simulated result shows that the brush grinding can be changed from elastic–plastic deformation to chip formation when increasing the brush speed to 1000 r/min. The normal and tangential forces increase linearly and quadratically with the increase in the rotation speed (500–5000 r/min), respectively, and increase linearly with the increase in the penetration depth (0.1–1 mm), which is consistent with the experiment results. In addition, the amount of material removal initially increases with the increase in penetration depth, and then decreases. This paper provides a new approach to understanding the process of material removal and is helpful for the selection of reasonable brush parameters in the intelligent grinding control application.