# A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings
## 1 Introduction  
Wheel hub bearings are one of the key parts of automobiles. Their main function is to bear the load of the automobile and provide precise guidance for the wheel hub rotation, which means that the wheel hub bearings not only bear the axial load but also bear the radial load. To ensure maximum safety and reliability, early warning of bearing wear faults is required. Bearing wear faults include frictional noise during rotation and abnormal deceleration of the suspension wheel hub during turning. There are many moving parts and rotating parts in contact with non-rotating parts, which may generate noises. If it is confirmed that the noise comes from the wheel hub bearings, the bearing may have been damaged, and need to be replaced. The bearings play a crucial part in reliable operation of rotating machinery in manufacturing systems. There is a growing demand for smart prognostics of wheel hub bearings' remaining useful life (RUL). It is necessary to monitor the wheel hub bearings by prognosticating the RUL, especially in the initial stage of the wheel hub bearing's running stage. The best time to repair or replace the wheel hub bearings can be obtained with the RUL. Considering the aging law, degradation law, influence factors, and determining factors of wheel hub bearings, the RUL can be prognosticated to grasp the current condition and life expectancy of the wheel hub bearings. Therefore, the risk of critical accidents can be prevented and maintenance costs can be saved. The safety hazards of the wheel hub bearings can be better removed, and the safety of the staff can be better protected too. A number of factors influencing the fatigue life of failure of the wheel hubs of an aircraft were analyzed [1]. Experimental characterization of mechanical vibrations and acoustic noise generated by defective automotive wheel hub bearings were measured and data were analyzed with a number of signal processing methods in frequency and time-frequency domains [2]. [PERSON] et al. proposed a fault diagnosis algorithm of the unit based on immune danger theory [3]. [PERSON] et al. discussed the wheel hub fatigue performance under non-constant rotational loading [4]. Further, the bearing fault evaluation is carried out for structural health monitoring, fault detection, failure prevention, and prognosis [5]. The measurements methods, advantages, and disadvantages of fault diagnosis methods were discussed for bearing health monitoring through shaft signals [6]. [PERSON] et al. proposed a deep feature optimization fusion method to extract centrifugal pump bearing degradation features from massive amounts of vibration data [7]. The multi-time scale method is used for bearing defect tracking [8]. [PERSON] et al. described the vibration-based bearing and gear health indicators [9]. [PERSON] et al. investigated the possibility of a bearing health assessment method using local characteristic-scale decomposition, approximate entropy, together with manifold distance [10]. These achievements laid the foundation for bearing RUL prognostics. The rule-based methods for bearing RUL prognostics are typically designed using Weibull model and Finite Elements. [PERSON] et al. designed a Weibull model of the RUL for steel rebar considering corrosion effects [11]. [PERSON] et al. proposed a forced response prediction for turbine blades with flexible dampers [12]. [PERSON] et al. analyzed the intentional mistuning effect in the forced response of rotors with aerodynamic damping [13]. [PERSON] et al. discussed the parameter optimization to avoid propeller-induced structural resonance of quadrotor-type unmanned aerial vehicles [14]. However, these methods have too many influence indicators, and the calculation is too complicated. The mathematical expressions are various and difficult to unify. When the equipment working environment is complex and mutative, it is hard to imply the rule and the model is also hard to design.