# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
## 1 Introduction  
The common deployment of rotating machines is still increasing since they are the heart of production systems in a wide variety of industries, such as manufacturing tools [1], electric motors [2, 3], wind turbines [4, 5], aero-engines [6, 7], mining machines [8, 9], marine propulsions [10, 11], and autonomous vehicles [12, 13]. They frequently operate from 1 to 10,000 rpm in a harsh working environment and under different operating conditions and are exposed to faults that lead to failure. The presence of failure could mean several days or weeks of lost production and millions of dollars in downtime and repair costs, and reliability and maintenance are key for equipment assessment [14]. According to a GE power company study, a mid-sized LNG facility suffers costs of $4 million/day through unplanned process downtime. On a global scale, unexpected shutdowns in the process industry cost 5% of total annual production, representing more than $30 billion per year [15]. Although recent progress leads to higher efficiency and improves the durability of rotating machinery, they are still vulnerable to various problems. Most degradation processes are accompanied by reduced performance levels, which result in reduced safety and catastrophic failure. To minimize the unexpected failures and to ensure maximum asset utilization, it is fundamental to monitor the health condition of rotating machines through active condition-based monitoring (CBM) and prognostic strategies. CBM is a type of predictive maintenance that allows for monitoring the actual condition of rotating machines while they are in operation. It aims at improving the efficiency ofsystems by increasing the reliability of machines, by reducing machine-related incidents, and by avoiding unplanned downtime. One of the most critical tasks in CBM is the so-called prognostics health management (PHM). It identifies fault severity and predicts the remaining useful life (RUL) of the target system based on the degradation trends obtained through the condition monitoring data (CMD) analysis. According to the predicted RUL, it is then necessary to schedule an optimal maintenance strategy and repairs that do not impact production goals. Thus, with the use and application of statistical data analysis, inspection and reliability calculations can be achieved [16]. The squirrel cage induction motors (SCIMs) are a vital part of rotating machines in industrial applications. These motors have advantages such as robustness, simplicity of construction, high reliability, and operational safety. However, due to the specific function requirement, SCIMs are subjected to various stresses acting upon the winding, rotor, bearings, and shaft causing subsequent failures. Consequently, they always have a higher fault rate compared with other components, and the fault of rotating machines directly or indirectly causes most maintenance costs. Many condition-based reliability studies are focused on component-level prognostics, which allow the progression failure of critical rotating components to be predicted. Due to the relatively large application of SCIMs, the focus of this paper is on the rotor, which shares 10% of induction motors (IMs) faults [17]. The rotor is supported on two bearings: one at the drive end and another at the fan end. It is composed of bars embedded in the frame slots and shorted at both ends by the end-rings. Rotor defects can occur in the form of broken bars or cracked end-rings, rotor misalignment, mass imbalance, and rotor eccentricity. Common causes of rotor failure include a combination of mechanical and thermal stresses, load imbalance, vibration, and excessive temperature. The rotor bar is the main driving shaft in an IM through which torque and speed are transferred. According to the statistics collected by the Institute of Electrical and Electronics Engineers (IEEE) and Electric Power Research Institute (EPRI), the rotor bar faults are responsible for 8-9% of failure in an IM [18].