## 1 Introduction  
Tool condition monitoring (TCM) is of great importance to provide cost saving and reduce downtimes of machine tools [1; 2; 3]. Thus, a vast amount of research has been focusing on TCM and tool condition prognostics approaches for the machining process [4; 5; 6]. Results show that a TCM-based optimization of cutting parameters during machining can extend the remaining useful life of cutting tools [7; 8]. In the existing TCM system based on a data-driven model, according to whether the data processing model needs training data, it is mainly divided into a supervised TCM system and an unsupervised TCM system. A supervised TCM system requires more or less teacher data to train model parameters, such as Hidden Markov Model [9; 10], Neural Network [11; 12; 13], SVM [14; 15], Deep Learning [16; 17], etc. The Hidden Markov Model (HMM) is a Markov chain and a production model. It can be used to establish a stochastic process model describing tool states based on training data, which has a strong generalization ability. [PERSON] et al. [18] proposed a new tool wear condition monitoring method based on multi-scale mixed hidden Markov model analysis of cutting force signals. However, one of the limitations of HMM in the application is that the construction of the model requires a large number of samples to support. Tool pattern recognition based on a neural network uses a structure of networked topology to express the mapping relationship between features and tool wear. Different networked structures show different mapping capabilities and nonlinear fitting capabilities. [PERSON] et al. (2019) designed a convolution neural network algorithm to deal with the optimized features to achieve tool wear prediction. [PERSON] et al. (2019) proposed a tool wear monitoring method based on short-time Fourier transform and deep convolution neural network. Compared with neural network models, Support Vector Machines (SVM) can achieve better generalization ability when training samples are limited. [PERSON] et al. (2019) proposed a new method to monitor tool wear in the micro-milling process using the SVM artificial intelligence model, vibration and sound signals. [PERSON] et al. (2019) developed multistage SVM to identify tool wear based on the selected features. However, the performance of the SVM model largely depends on the selection of penalty factors and kernel function parameters. The above-mentioned models require feature extraction of the original signals during the cutting process before use, so the monitoring quality is also significantly affected by the effectiveness of the features. In contrast, deep learning algorithms can achieve automatic feature extraction through autoencoders, thus they are able to find more effective features to characterize tool states. [PERSON] et al. (2019) proposed a tool wear monitoring method for milling complex parts based on deep learning. However, this also increases the nonlinear degree of the system, requiring more training data to improve the accuracy of identification. In the machining process of some complex parts, sufficient training samples cannot be obtained due to high manufacturing cost and fast tool wear, so it is difficult to apply and popularize the supervised TCM system. Different from supervised methods, unsupervised TCM systems establish model parameters only by using monitoring signals rather than training data ([PERSON] et al., 2019). However, there are few reports on TCM research based on unsupervised methods due to their poor nonlinear fitting ability and real-world supervising ability. [PERSON] et al. (2019) proposed an unsupervised approach for degradation prognostics of machine tool components and consumables. However, this was not a completely unsupervised method although a clustering algorithm was used as the core algorithm, since the model should be trained on the selected samples and their respective feature distributions. Especially, a retraining process was required when cutting parameters changed. [PERSON] et al.