# Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology
## 3 Failure Physical Analysis for Signal Selection
### Application of Multi-Sensor Information Fusion  
Through the study of the mechanism of machining equipment failure, it can be concluded that the damage of the machining equipment is usually the result of a combination of various factors. It is a complex physical phenomenon caused by high temperature, high pressure and huge intermittent impact. Therefore, machining equipment damage is usually accompanied by a variety of signal changes. The application of multi-sensor information fusion is mainly to make an accurate judgment and prediction of the state and real-time reliability of the machining equipment through the comprehensive processing and analysis of various signal data. Moreover, according to the above analysis, various sensors may have certain defects. Therefore, it is usually required to be used simultaneously with other sensors. The signals are mutually verified, so that the processing results are more reliable. In the open technical literature, multi-sensor information fusion first appeared in the late 1970s. In 1988, the US Department of Defense listed fusion technology as one of the key technologies developed in the 1990s. In 1998, the International Information Fusion Society was established, which promoted the development of source-aware information fusion technology [59]. According to the three abstract levels of multi-sensor information, the level of information fusion can be divided into three levels: Data set fusion, feature set fusion and decision-level fusion. In the fusion algorithm, weighted average method, maximum likelihood estimation, cluster analysis, support vector machine, neural network method, genetic algorithm, Kalman filtering method, Bayesian estimation method, fuzzy set theory, DS evidence theory and other algorithms are formed. In terms of application, at the 23 rd Japan International Machine Tool Show in 2006, an intelligent spindle was exhibited by Yamazaki Mazak Company of Japan. The spindle is equipped with various sensors such as temperature, vibration, displacement and distance to protect and warn the spindle. The development goal is to automatically optimize the parameters such as the spindle speed in the future according to the processing conditions [60]. Figure 4: Type of AE signal generated during tool damage. At present, multi-source sensing information fusion technology has been widely used in military and civilian fields, including fault diagnosis, manufacturing process detection and control. Therefore, researchers have also continuously explored the state of tooling in the fusion of multiple signals. [PERSON] et al. have used vibration and visual sensors to detect and identify the wear condition of tools [60]; [PERSON] et al. [61] have proposed the combination of the various sensor signals or at least the sensor information of multiple sensors to evaluate the tool machining state and tool failure; [PERSON] of the University of Miami in the United States studied multi-sensor based tool condition monitoring for multi-coated multi-layer carbide end mills for 4340 steel, using multiple sensors for force, vibration, acoustic emission and spindle power sensors. The experimental results show that the TCM design based on feature level fusion can significantly improve the accuracy of tool state classification. [PERSON] et al. have applied multi-source sensing information by neural network, group clustering and least squares regression method, and compare the performance of the monitoring system. The experimental results show that the tool monitoring system with multi-source sensor is applied. The accuracy is improved over a single sensor. [PERSON] [62] et al.