## 1 Introduction
Since the FFT is essentially an integral over time, it fails to do so for non-stationary data, which could result from intermittent defect or evolutionary faults [18]. To address the FFT limitation, time-frequency signal-processing tools such as the short-time Fourier transform (STFT) [19], Hilbert-Huang transform (HHT) [20], Wigner-Ville distribution (WVD) [21], and wavelet transform [22] are introduced. The wavelet transform is a relatively new and powerful tool, able to perform a local analysis of a signal and revealing some hidden aspects of the data that the other signal analysis fails to detect [23]. In this work, the wavelet transform was selected for signal processing to detect changes in vibration signatures that are caused by the faulty components. Once the raw signal is acquired and processed, feature extraction techniques should be employed to extract the representative features that are used for _HI_ construction. Feature extraction methods could be roughly classified into model-based methods and data-driven methods [24]. [PERSON] et al. [12] obtained a bearing _HI_ by fusion of vibrational signal variance from the time domain and Choi-Williams distribution from the time-frequency domain. [PERSON] et al. [25] presented a method to extract multiple features from the vibrational signal with multiple signal processing techniques, and then these features are selected and weighted to form the new _HI_. In [26], the authors implemented the discrete wavelet packet transform to decompose the raw signal into different sub-bands, and the _HI_ was extracted from each signal. Although model-based methods do work and achieve an extraction of an accurate _HI_, they still have two deficiencies: (1) Feature selection is heavily dependent on prior knowledge and diagnostic expertise. Moreover, it often focuses on a specific fault type, and thus it may be unsuitable for other faults [27; 28]. (2) In real industries, acquired signals are usually exposed to environmental noises, and are transient and non-stationary. Therefore, signal processing technologies need to be employed to filter the collected signals, which can result in a loss of information [27; 29]. Data-driven methods attempt to extract features from measured data using machine learning techniques. In recent years, deep learning has emerged as a powerful tool to extract the representative feature from the collected signals [30; 31]. Different deep learning architecture, including convolutional neural network (CNN) [29; 32], recurrent neural network (RNN) [33], autoencoder [27], and generative adversarial network (GAN) [34], are successfully used to extract features automatically. The greatest advantage of deep learning is that it needs no prior expert knowledge and represents more accurate features [35]. Until now, most studies employ deep learning methods in a supervised setting to extract features for classification problems. For this purpose, performance data of different degradation levels of a component are prerequisites to creating labeled healthy and unhealthy datasets. However, gathering different degradation-level data requires large failure data, which is not available in practice, especially for high-reliability component [36]. On the other hand, a recent review on the state of deep learning on PHM [37] revealed that studies from a health-management point of view have been rather limited, largely due to the unavailability of fault data. Moreover, many implementations of deep learning models in the literature are still constrained to specific equipment or applications and are not reusable when the predefined conditions change. In order to address the aforementioned restrictions, developing a single framework that can systematically be extended to all aspects of system health management is necessary [3; 36]. This framework has to be able to be trained on-line without requiring historical data, and must use only healthy operational data for training [3].