###### Abstract
The validity of the model was verified using milling tool life data. [PERSON] et al. [19] proposed a convolutional long and short-term memory model for bearing RUL prediction. Unlike the traditional method that directly connects the convolutional neural network (CNN) and LSTM, this method performs convolutional operations on all the state transitions in the LSTM. [PERSON] et al. [20] proposed a temporal convolution prediction framework with a residual self-attention mechanism. The marginal spectrum of the vibration signal is first extracted; then, it is used as the input of the temporal convolution network, and finally, the residual self-attention mechanism is introduced to achieve end-to-end remaining lifetime prediction. The validity of the proposed method was verified with the PHM2012 challenge dataset and the XJTU-SY dataset. [PERSON] et al. [21] proposed a prediction method that combines a one-dimensional convolutional neural network with simple recurrent units. The extraction of health indicators (HI) is a crucial step in the RUL prediction process. HI mainly reflect the degradation of mechanical equipment. Therefore, the quality of HI will directly affect the RUL prediction accuracy. [PERSON] et al. [22] used self-organizing mapping (SOM) to fuse the extracted features to construct the HI of rolling bearings. [PERSON] et al. [23] used the root mean square (RMS) error as the HI of a rolling bearing and predicted the future HI sequence using a gated dual attention unit neural network to determine the magnitude of the rolling bearing RUL. [PERSON] et al. [24] proposed a simple HI construction scheme that uses the ratio of the current moment's RUL value to the initial RUL as the HI, reducing the need for expensive prior knowledge. Five bandpass energy values of the spectrum are used as features. The mapping relationship between the features and HI is constructed directly using a circular autoencoder (AE) based on the attention mechanism. Finally, the value of the RUL is calculated using linear regression. [PERSON] et al. [25] used CNN to extract the bearings' HI and used outlier region correction techniques to detect and correct the outlier points in the obtained HI. There are three main problems with the above methods. (1) In the HI construction process, it is often necessary to analyze the vibration signals in the time-frequency domain and extract the features in the time domain, frequency domain or time-frequency domain as the input to the model. However, different combinations of features may produce different results. In the literature [26; 27], 14 specially designed features and 14 commonly used features were used for HI construction, respectively, and the trend, monotonicity, and scale similarity of the two HI differed. This approach requires a large amount of expert prior knowledge and does not allow automatic HI extraction. (2) The temporal information of the vibration signals is not fully utilized, and usually, only a single size time window is used for feature extraction of the vibration signals, which cannot utilize the local and the global information comprehensively. The bearing vibration signal is a vector containing time information, and the lack of adequate consideration of the time scale can affect the effectiveness of HI. (3) Usually, when using HI for RUL prediction, the failure threshold of HI needs to be determined. In the literature [23], the RMS maximum value of 1.903 at bearing failure was chosen as the failure threshold. In the literature [28], the failure threshold for HI was determined to be 0.17 by calculating the average of the last five local minimum HI points of the tested motor. These methods often carry a particular element of subjectivity and conjecture, leading to errors in RUL predictions. To solve the problems mentioned above, a novel deep learning framework, called a multi-scale convolutional autoencoder (MSCAE), is proposed in this paper for the automatic extraction of rolling bearing HI.