## 6 Service Safety Evaluation of the Pick Based on Wear and Fatigue  
### Pick Wear  
The wear of the pick is important for estimating the downtime of the cutting machines [121]. The degree of pick wear greatly affects the cutting efficiency, energy consumption and service life of the shearer drum. Therefore, the quantitative evaluation of pick wear is of great theoretical significance to the determination of pick deterioration grade and prediction of pick service life. In general, pick wear is mainly measured through the weight loss method [183] and three-dimensional morphology recovery method [184; 185]. Traditional three-dimensional morphology recovery technologies, i.e., the shape from shading method, stereovision method and structured light method, have limitations in obtaining accurate morphologies [185]. However, the focused morphology recovery technique has the advantages of non-occlusion, strong practicability, high accuracy and a simple algorithm, which can make up the limitations of traditional three-dimensional morphology recovery technologies. In order to obtain the three-dimensional morphology of pick cone and pick wear rate, the pick wear measurement system has been established combining the normal distribution operator filtering based on image processing method, improved focus evaluation function, focused morphology recovery method, and optimization design method. Figure 4 shows the pick wear measurement system based on the focused morphology recovery technique [16]. [PERSON] et al. [186; 187] investigated the effects of the skew angle of a pick cutter on its wear phenomenon using a frictional ignition test machine, and they reported the importance of the skew angle on the wear behavior of a pick cutter.  
During coal rock cutting, several characteristics (acoustic emission signal, vibration signal, current signal, power signal, etc.) can be employed to indirectly reflect the degree of pick wear. It is proposed that the picks with distinct wear degrees exhibit different characteristics of acoustic emission signals and vibration acceleration signals during coal-rock cutting [188; 189; 190]. It is indicated that the amplitudes of vibration acceleration and the acoustic emission signal increases with increasing wear degree. Furthermore, many scholars have established wear models of shearer picks using EDEM [191; 192], ABAQUS [193], ANSYS [194] and Hyperworks [195]. Correlation models between operating parameters (rotational speed, installation angle and pick tip angle) and pick wear degree are established  
Figure 3: Development of coal rock cutting test rigs by the pick.  
to provide the theoretical basis for the quantitative evaluation of pick wear [196,197]. Meanwhile, the infrared temperature and current signals of picks with distinct wear degrees are measured during coal rock cutting tests in order to establish the sample database of multi-feature signals, which can realize the online detection of pick wear degree [198, 199, 200]. The training and learning of samples of multi-feature signals are conducted to establish the neural network feature signal fusion model (Figure 5) to realize the online monitoring and accurate identification of pick wear degree [201]. In addition, X-ray technology and support vector machine technology have also been employed in the detection and prediction modelling of pick wear degree [44,149,202,203]. [PERSON] et al. [204] proposed that pick wear mechanism was the abrasive wear attributed to the small amount of hard materials such as A1203 in the coal seam containing gangue. [PERSON] [203] studied the relationship between the rock Saichal friction coefficient and physical and mechanical properties of the rock, evaluated the rock abrasiveness and predicted the pick wear loss.  
Figure 4: Pick wear measurement system.  
Figure 5: Neural network feature signal fusion model of pick wear degree.