# A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes
## 3 Non-Destructive Detection Method for Steel Wire Ropes
### Optical Detection Method  
Optical detection method is an efficient non-destructive detection method, which has been developed in the field of WR surface damage detection. It can intuitively grasp the damage situation of WR surface. At the same time, with the development of WR oil pollution removal method/device [41], the influence of oil pollution gradually decreases.  
The principle of optical detection method is shown in Figure 3, which generally includes two parts: image acquisition and damage diagnosis. The image acquisition generally uses high-speed camera to collect the surface image of WRs, and the damage diagnosis includes image preprocessing and pattern recognition. WR damage detection based on pattern recognition is a potential research direction. Specifically, the hardware and software of the image acquisition system are configured according to the actual situation; the collected images are first processed by pretreatment method like cutting or filtering. Then, feature extraction methods (such as local binary pattern and gray level co-occurrence matrix) are used to extract features, and next dimensionality reduction is carried out on the established feature datasets. Finally, machine learning classifiers (such as support vector machines, artificial neural networks, etc.) are used for training and testing to obtain robust classifiers for state recognition of unknown images [42].  
Based on camera and offline processing algorithm, [PERSON] et al. [43] established a measurement system which measured the change of rope length according to the characteristics of rope twist to detect the damage of rope. For elevator rope, [PERSON] et al. [44] proposed a fault monitoring method based on image processing and autocorrelation analysis, which realized the defect detection of steel  
Figure 3: Optical detection principle for steel wire rope.  
rope to a certain extent. In [PERSON] et al. (2019), [PERSON] et al. realized the identification and segmentation of WRs boundaries through image processing and clustering methods. [PERSON] et al. [PERSON] et al. (2019) proposed using Hidden Markov field model to locate the defects of WR, the results demonstrated that the detection performance of this method is better than that of the previous time invariant system classification method. Then, they compared the performance of different texture features in the detection of WR surface defects in [PERSON] et al. (2019). For the defects of WR, the histogram of oriented gradient feature was the best, followed by the local binary patterns feature. In the work of [PERSON] et al. (2019); [PERSON] et al. (2019), [PERSON] et al. established a probabilistic appearance model as a representation of normal surface changes combined with the structure and appearance of the WR and realized the detection of WR abnormal surface. For surface defects of thin metallic wires, an automatic optical detection technique was presented by [PERSON] et al. in [PERSON] et al. (2019). This technique was based on the intensity variations on the scattered cone generated when the wire is illuminated with a beam at oblique incidence.