# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
## 2 Related Works  
The rotor bars of a SCIM can be cracked due to stress and/or improper rotor geometry design. Once a bar breaks, irregularly distributed rotor currents are present. These currents cause unbalanced stator current, power, torque pulsation, and speed. Besides these parameters, others, such as sound acoustic and vibration, can reveal an IM's failure. Several studies have carried out diagnoses of IMs using motor current signature analysis (MCSA). [PERSON] et al. proposed a method to diagnose rotor bar failures in IMs based on the analysis of the stator current during start-up using the discrete wavelet transform (DWT) [29]. [PERSON] et al. proposed a method based on the identification of characteristic patterns introduced by fault components in the wavelet signals obtained from the discrete wavelet transformation of transient stator currents [30]. In [31], the advanced use of wavelet analysis was introduced by analyzing an axial vibration signal for rotor under broken bars faults detection by removing the effects of the interference frequency components. In [32], the authors used the Zhao-Atlas-Marks (ZAM) distribution to investigate the broken rotor bar faults diagnosis based on vibration transient signals. In [33], the authors demonstrated that, looking at the shape of torque spectrum, it is possible to detect if the unbalance is induced by the broken rotor bars or by unbalanced stator windings. Furthermore, they proved that a defective rotor causes a double-slip frequency in the air-gap torque spectrum while a double fundamental frequency can occur for a faulty stator. [PERSON] and [PERSON] [34] studied the effects of rotor eccentricity on the acoustic emission spectrum from an IM. They showed that the slot harmonics in the acoustic spectra of IM are a function of static eccentricity. The accuracy of fault detection using acoustic measurements is reduced because this monitoring signal contains health condition monitoring as well as noise. Fault detection and diagnosis are processes that aim at determining fault presence in the IMs in an earlier stage and at identifying the types, locations, and severity degrees of faults. They are well developed and spread within the research and the industrial communities, as reviewed by the previously cited works. [PERSON] et al. [35] proposed a condition monitoring approach for induction motor bearings. The proposed method uses a deep learning architecture to extract an expressive representation of the bearing state degradation from the stator current signals. In [36], the authors combined data-driven and model-based approaches to estimate the RUL of rolling element bearings using regression-based adaptive predictive models. Looking at the evolving trend of the bearing HI, the proposed approach addresses the issue of determining the time to start prediction (TSP) and the time to reach a prefixed dynamic failure. In [28], a novel prognostic approach to estimate a bearing RUL is developed. The proposed approach uses Hilbert-Huang entropy to construct a suitable HI based on vibration signals. Then, once the HI is constructed, a linear degradation model has been used to predict the bearing RUL. The experimental results demonstrated that the performance of the proposed approach is not the best through the use of vibration data contaminated by unwanted noise. [PERSON] et al. [37] compared two different strategies: one with many sensors and the other without any sensor for ball screw PHM analysis. The authors of this research focused on early diagnosis, health assessment, and RUL prediction. The results obtained prove that the vibration signal shows a clear exponential degradation trend of the system, but it is a bit less sensitive than the torque signal diagnosis emergent faults and that the built-in torque signal is valuable for faults diagnosis and incipient failure identification. [PERSON] et al. [20] developed an effective remaining useful life prognostics method for rolling bearing.