# Imbalanced Diagnosis Scheme for Incipient Rotor Faults in Inverter-Fed Induction Motors
## 1 Introduction  
Condition monitoring is a growing concern in predictive maintenance tasks to prevent unexpected machinery breakdowns and production losses. Consequently, strengthening the ability to detect and diagnose machine faults can reduce costs in modern production planning and operations. As a primary energy consumer, induction motors (IM) are a crucial element in electromechanical equipment and currently dominate in both the industrial and commercial sectors [1]. Therefore, a well-designed condition-based monitoring system can minimize maintenance costs and correctly target necessary maintenance tasks [2]. Motor current signature analysis (MCSA) has been preferred for extracting fault signatures at both transient [3] and stationary regimes [4]. This technique has several advantages over vibration analysis, such as remote sensing, low implementation cost, and online monitoring. Furthermore, voltage source inverters are increasingly used to feed IM in variable speed applications. This, however, introduces a new set of challenges. Using inverters introduces undesired harmonics into the stator current, negatively altering the extraction of features based on spectral analysis. Consequently, many diagnosis techniques that worked for line-fed IM do not perform appropriately on inverter-fed IM. As a result, the methodologies proposed to date as well as novel ones have to take into account this challenge to avoid the loss of performance of the diagnosis systems. Nowadays, automatic diagnosis through machine learning techniques is one of the most promising fields for this challenge. Automatic diagnosis has been conceived as atool for improving the efficiency and reliability of a balanced or unbiased fault detection scheme. In fact, an important research community has proposed supervised classifiers to diagnose the health of the electric machine [5; 6; 7]. Basically, a classifier consists of a function (linear or nonlinear) that tries to separate the classes defined in the problem by minimizing the expected loss. In other words, it tries to identify which class corresponds to each observation with the lowest classification error. The different motor states form classes of the problem to diagnose. Hence, it is clear that data-driven approaches oriented to an automatic diagnosis have emerged recently [8]. Authors in [9] demonstrate an interest in data-driven methods where actual field data are obtained from railway electrification networks. The data is continuously collected from the most important variables, which gives a data-driven detection approach for low-frequency oscillation events in alternating current railway catenaries. In addition, there are works where the simulation-driven machine learning training phase has also been developed by generating raw input data. This kind of approach tries to overcome limitations such as data scarcity and data imbalance [10]. Data imbalance is a crucial concern, since actual datasets are not entirely balanced [11], and it is common for actual industrial data obtained both from destructive tests of the machine and during its useful life to be imbalanced [12]. When the minority class representation is extremely low, the problem is usually denominated as an anomaly detection case [13]. Unlike the case of balanced classification, under an imbalanced scenario, the classifier directly labels new observations to the majority class because of the existing inherent bias as the imbalance proportion between classes increases. This fact is detrimental to the performance of diagnosis systems and requires much more attention. To deal with class imbalance, the data science literature has presented several techniques relating to the following approaches: embedded methods, data processing, and ensemble methods. Within data processing approaches, resampling methods have offered adequate characteristics to balance previously imbalanced training sets.