# A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings
## 1 Introduction
The data driven method for bearing RUL prognostics has shown promising potential to support smart prognostics and to avoid the design of the complicated mathematical expressions. Many data-driven methods were proposed for the RUL estimation of rolling element bearings, including support vector regression [15], sparse representation theory [16], adaptive Wiener process method [17], the time series method with Linear and nonlinear techniques [18; 19], nonlinear autoregressive neural network [20; 21], localized maintenance method [22], sparse representation method [23], physics-based algorithm [24], the rain flow cycle counting method [25], and grey theory [26; 27]. These RUL estimation methods give static estimations of RUL. To dynamically prognosticate the RUL, dynamical data-driven methods were proposed for further RUL prognostics of rolling element bearings. Mixed effect methods were proposed for prognostics of rolling element bearings [28]. Goodness metrics of correlation, monotonicity, and robustness are defined and combined in more relevant degradation feature selection for RUL prognostics [29]. The state-space method of linear Brownian motion was proposed to make the drift coefficient adaptive in RUL prognostics [30]. [PERSON] et al. designed a deep-learning-based prognostics framework for bearing RUL by using a deep auto encoder and deep neural networks [31]. [PERSON] et al. derived an auto regressive method to prognosticate its RUL according to healthy bearing vibrational signals [32]. [PERSON] et al. proposed a data-driven method based on Gaussian mixture method and distance evaluation technique to prognosticate the RUL of rolling bearings [33]. [PERSON] et al. proposed a performance degradation method and a real-time RUL prognostics method on the basis of temperature characteristic parameters to determine the RUL of wind turbine bearings [34]. [PERSON] et al. developed a particle filtering-based method to prognosticate the RUL of rolling element bearings [35]. [PERSON] and [PERSON] designed time-frequency image features to construct health indicator (HI) and prognosticate the RUL based on continuous wavelet transformation and deep learning [36; 37]. [PERSON] et al. proposed a nonlinear degradation-based method and model-based method for RUL prognostics of rolling element bearings [38; 39]. [PERSON] et al. designed a hybrid prognostics method for RUL prognostics of rolling element bearings [40]. [PERSON] et al. proposed an open and data-driven-based status assessment method to obtain the individual status offset in real-time bearing residual life prognostics [41]. These RUL prognostic methods can dynamically prognosticate the RUL in real time. The above methods focus on the RUL prognostics of bearings. For the RUL prognostics of wheel hub bearings, there are still many problems, as follows: (a) The above-mentioned methods assume that the data used in RUL prognostics is composed of one specific fatigue damage type. The actual data usually contains two or more fatigue damage types, so the specific method for specific-type fatigue damage cannot fit compound fatigue damage. (b) These methods assume that the data used in RUL prognostics is accurate. The RUL of wheel hub bearings has a great correlation with its working environment, but we cannot ensure the prognostic accuracy of RUL in complex working environments. The RUL of wheel hub bearings is affected by many factors, and the fault signals of wheel hub bearings are submerged in different kinds of noise. The premise of the above-mentioned methods is that the obtained data is accurate and has no noise. It is very difficult to obtain such data in the actual working environment. (c) These methods of RUL prognostics are conducted in the short term. However, more often, long term RUL prognostics are required to ensure maximum safety and reliability, especially in the early running stage of wheel hub bearings. To solve these problems, it is necessary to design a generalizing RUL prognostics method.