## 1 Introduction
It has the following advantages: the ability to determine the number of mode decompositions, adaptively determining the mode decomposition based on the actual situation, and effectively separating intrinsic mode components (IMF) in the signal domain. This allows for obtaining the effective decomposition components of the given signal and the optimal solution to the variation problem. [PERSON] et al. used adaptive multi-sensor data fusion to extract the best features and used CNN for planetary gearbox fault diagnosis [42]. [PERSON] et al. proposed a robust fault diagnosis method, introducing noise labels to improve the generalizability of the model [43]. [PERSON] et al. extracted fusion sequence features with an adaptive convolution kernel of atrous convolution [44]. [PERSON] et al. developed a new type of adaptive normalized convolutional neural network, and a batch normalization algorithm was used to eliminate differences in feature distribution [45]. [PERSON] et al., in order to improve upon the diagnostic accuracy and robustness, integrated time series data, feature extraction, and feature selection into the data fusion strategy. Comprehensive and representative fault features are obtained from multi-sensor signals to enhance the feature learning capability of the network [46]. [PERSON] et al. proposed an automatic speed adaptive neural network (ASANN) model for instantaneous rotational speed, which detects malfunctions of planetary gearboxes in different operating environments [47]. Gramian angular field (GAF) converts one-dimensional time series data into two-dimensional image data. Using the data as a CNN classifier input can improve the accuracy of model predictions [48; 49; 50]. Although the existing mechanical fault diagnosis methods have high accuracy in model prediction, there are still two problems encountered in actual field production. One is that the accuracy drop caused by different manufacturing situations and the noise in terrible environments is too huge for general application. The other is that the variation of fault features in precision machining is usually subtle so it is difficult to use a single physical signal to obtain all the characteristic changes. Thus, it is difficult to improve the accuracy of the model. This paper proposes a method based on an adaptive deep convolutional neural network (ADCNN) to target the aforementioned issues that cause declines in accuracy. Experiments will be conducted using dental milling cutter malfunction classifications as an example in order to verify that the method has an adaptive function and is more robust than other popular fault diagnosis models. This method differs from the traditional method in that it combines multi-sensor data fusion and feature fusion mechanisms with deep CNN. The fusion features can strengthen adaptively, which improves the noise interference that affected the accuracy of the prediction model. The remainder of this paper is as follows. Section 2 briefly introduces the theories of CWT, Gaussian filter, GAF, and CNN. Section 3 discusses the procedure and model design approach of the proposed method. In Section 4, the experimental setup, data, and planning of the case experiments are described, the effectiveness of the proposed method is verified, and the test results are discussed. Section 5 discusses the conclusions.