## 1 Introduction
[PERSON] et al. [19] analyzed vibration signals using the Hilbert-Huang transform and developed a HI using support vector machine and support vector regression (SVR). [PERSON] et al. [20] extracted textual features of time-frequency representation and predicted RUL by transforming high-dimensional features into low-dimensional features using principal component analysis and linear discriminant analysis (LDA). [PERSON] et al. [3] proposed the use of the weighted minimum quantization error (WMQE) as a HI, which is a fusion of 10 time domain features, 16 time-frequency domain features, and two features based on trigonometric functions. While most of these studies relied on feature extraction, selection, and fusion techniques to construct the HI, these complex processes are labor-intensive. Additionally, HIs constructed using shallow models (e.g., SVR or LDA) lack prognostics capability and adaptability [6]. To solve these problems, various deep learning techniques (e.g., convolutional neural networks (CNNs), deep belief networks, and recurrent neural networks (RNNs)) have been employed to build advanced HIs for big data. [PERSON] et al. [21] proposed an integrated deep learning approach-based HI by fusing both time and frequency domain features. The method captured high-quality degradation trends of rolling bearings from massive data. [PERSON] et al. [2] extracted 11 time domain features, five frequency domain features, and eight time-frequency domain features. The most sensitive features were selected using monotonicity and correlation metrics. The selected features were fed into an RNN architecture to construct a HI. The RNN-HI has proved to be superior to the SOM-based HI. These studies improved prediction performance using the deep learning approach, but expert knowledge or feature engineering is still required. Therefore, instead of extracting and fusing multi-domain features, researchers have adopted CNN to automatically discover useful features from the raw signal to construct the HI. The CNN was first proposed by [PERSON] et al. [22] and has led to considerable advancements in image processing and speech recognition. [PERSON] et al. [5] developed convolutional bi-directional long short-term memory (CBLSTM) networks to predict tool-wear conditions. They extracted local features using the convolutional and pooling layers of the CNN from the raw signal. The features are inputted into long short-term memory (LSTM) neural networks to construct the HL [PERSON] [23] also directly inputted the raw signal to a one-dimensional (1-D) CNN, which incorporates feature extraction and fault detection into a single algorithm. Although the CNN was originally designed to process two-dimensional (2-D) visual signals, most of the research in the prognostics field has been used to process 1-D vibration signals. To improve the feature extraction of the CNN, another study has been conducted using a 2-D CNN based on the time-window approach after converting multi-sensor signals into a 2-D form [1]. However, limitations still exist in using the raw signal instead of 2-D image data. Generally, the vibrational raw signal contains a large amount of noise and has nonstationary characteristics. Therefore, if the raw signal is used to learn, it can degrade the learning capability of the CNN [24]. To address these issues, we present a novel image feature-based CNN method that adopts the continuous wavelet transform (CWT) for HI construction and RUL prediction. The CWT performs a 2-D wavelet transform of the time-frequency domain to convert the vibration signal into a multi-scale power spectrum image. The power spectrum visually shows that the frequency components have changed over time. In addition, it does not require the analyst to have much experience in fault diagnosis or signal processing [25]. As a result, the CWT is widely used for mechanical fault diagnosis such as bearings and gearboxes [26; 27]. Therefore, we adopted the CWT to take the visual advantage of decomposing vibration signals and localizing faults.