# Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology
## 2 Multi-Source Information during Cutting Process
### Cutting Process Information of Machining Equipment
[6] have established a model between motor current and tool breakage to predict tool breakage. [PERSON] [7] has used stochastic fuzzy neural network to establish the corresponding relationship between tool wear and motor current. This model can identify the tool wear condition in a certain precision range. The Acoustic Emission (AE) phenomenon was discovered by German scientist [PERSON] in 1955. At present, AE technology has been rapidly developed in engineering applications and is recognized as a new monitoring technology with great potential. The Acoustic emission signals, which are high-frequency oscillations generated by the release of strain energy in the form of elastic stress waves when the material is damaged, can be detected by piezoelectric sensors [8]. In the machining process, many acoustic emission signals are generated, which are from the tool wear, the deformation of the workpiece, and the friction of the chip. The information of the tool wear is included in the acoustic emission signal, as shown in Figure 1[9]. As a key component of machining equipment, researchers have done a lot of research on cutting tools. At present, tool wear monitoring technology based on acoustic emission technology has been vigorously promoted, with the advancement of signal processing technology and the rapid development of computer technology. AE technology is recognized as a promising new monitoring technology. Compared with other monitoring methods, the acoustic emission signal has high sensitivity. Figure 1: Sources of Acoustic Emission (AE) in machining. The signal frequency (100 kHz-1 Mhz) is higher than the noise frequency, so it avoids the low frequency band (with serious noise) during processing. However, the cost of the sensor is high. Additionally, a high-frequency signal acquisition device is also required [10]. Many researchers have proposed monitoring programs under various processing scenarios. [PERSON] et al. [11] have used AE to perform tool wear detection during the cutting process. It is considered that the power spectrum of the AE signal increases with the wear of the tool within 350 kHz, and the sum of AE counts is closely related to the tool wear. The results of [PERSON] and others have shown that under normal wear and tear, AE mainly comes from the first, second and third deformation zones and is a typical continuous signal. But when the tool is damaged, the AE signal is a discontinuous signal [12]. The following studies use the AE signal for wear monitoring of drills, turning tools and milling cutters respectively. The AE signal can be used to meet the requirements of tool condition monitoring in many aspects. [PERSON] has proposed procedures to detect tool breakage and to estimate tool condition (wear) by using AE. The proposed procedure filters the AE signals with a narrow band-width, band-pass filter and obtains the upper envelope of the harmonic signal by using analog hardware [13]. [PERSON] et al. have studied the milling process of 45 steel, applying wavelet transform to multi-resolution decomposition of acoustic emission signals, and extracting the signal energy of each frequency band as the feature quantity [14]. [PERSON] et al. have studied a drilling process with different degrees of wear in the drill bit to find relationships between acoustic emission (AE) and torque measured during the drilling process, and also with the degree of wear of the tool [15]. [PERSON] et al. have studied the roughing process of Inconel, and have used wavelet packet transform to extract characteristic values such as skewness, kurtosis and energy of the AE signal and the wavelet component coefficient of the cutting force signal. The correlation coefficient method was used to optimize the kurtosis and energy, and then the neural network algorithm established a tool wear model based on cutting force and acoustic emission signals [16]. [PERSON] et al.