# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
## 1 Introduction
Once a bar breaks, the status of the neighboring bars deteriorates progressively and the current consumption increases by 50% of the rated current due to the increased stresses, which reduce the efficiency of the motor [19]. Given the impact and the high costs usually associated with rotor failures, CMD, health assessment, and data-driven prognostic strategies that can refine the RUL prediction of IMs have been investigated [20; 21; 22]. Data-driven approaches are not the only way to do this; there exist two other approaches, namely model-based and hybrid approaches [23; 24; 25; 26]. The model-based approaches use mathematical models to represent rotor behavior and degradation phenomena. This method is applicable when an accurate mathematical model could be developed from rotor failure or degradation modes. The second approach instead is based on the combination of both data-driven and model-based approaches. The data-driven approaches, also known as the data mining approaches, make predictions based on statistical models and hidden patterns without explicit mathematical models. They use historical data (i.e., vibration and acoustic signals, temperature, pressure, oil level, torque, currents, voltage, etc.) collected from sensors to automatically learn a model of machine degradation behavior [27; 28]. However, since the fault mechanism of the rotor is complex and not straightforward under varying operating conditions and operation loads, the model-based methods may not be sufficient to perform reliable rotor fault prognostics. This study aims to develop a prognostic strategy for broken rotor bars based on physics-based models and data-driven methods. To this end, we model the IM with a broken rotor bar using Matlab/Simulink. Using the proposed model, we collect synthetic data that can reflect the evolution of the degradation behavior. The main contributions of this paper are as follows: (1) Various condition-monitoring data are used to predict the RUL of broken rotor bar. The degradation behavior of the component is observed indirectly by monitoring specific degradation measures, such as torque, stator current, and speed. The evolving trends or condition indicators (CIs) in the data are determined using condition monitoring techniques based on time and frequency domains. (2) The most suitable CIs, which improve the accuracy of the predicted RUL, are selected using a combination of the three most popular measures, including monotonicity, trendability, and prognosability. Consequently, the first principal components obtained using principal component analysis (PCA) techniques can be used as broken rotor health indicators (HIs). (3) The constructed HIs are used to fit an exponential degradation model to derive the RUL of the degraded component. In particular, the impact of diversity in features and constructed HIs on RUL prediction accuracy is investigated. The rest of the paper is organized as follows. Section 2 introduces related works. Section 3 describes the failure modelization and the synthetic failure data sets using Matlab/Simulink. Section 4 details the proposed prognostic methodology and the RUL estimation based on the degradation model. Following that, a detailed description of the synthetic data manipulation process is presented in Section 5. In Section 6, the prognosis results are demonstrated using degradation datasets through simulations. Finally, we conclude this paper with the main findings and future works.