# Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification
## 1 Introduction  
Rotating electrical machines are responsible for converting a great amount of worldwide energy into mechanical energy [1]. Mobility, transportation, logistics, construction, production, agriculture, food, automation, and basically any economic activity and industry directly or indirectly depend on rotating electrical machines, as discussed in literatures, e.g., [2; 3; 4; 5; 6]. The rapidly evolving industries have suggested that we will be witnessing a further increase in this rate [7]. Furthermore, the increasing demand for hybrid and electric vehicles, the rapid transition toward automated systems and micro and nano mechatronics devises, increasing interests for more efficient energy conversion systems, and emerging new robotics machines have been motivating further advancement in the rotating electrical machines reported in the works of scholars, e.g., [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. One of the key factors of overall efficiency maximization covers the well-sized and high-efficient components [19; 20; 21; 22]. Therefore, the reduction and prediction of faults occurring in electrical machinesand drive systems such as electrical, thermal, and mechanical faults of electrical machines are strongly suggested to be essential [23; 24; 25; 26; 27; 28; 29]. Classical solutions of Fault Detection and Diagnosis (FDD) [30] are based on the complex mathematical models, e.g., supervised diagnosis [25; 26; 27; 28; 29; 31], or dynamic models [32; 33; 34; 35; 36; 37] of the processing system. Intelligent modernization has contributed to the widespread use of Machine Learning (ML) techniques in industrial applications [38; 39; 40; 41; 42; 43]. As a result, the latest FDD systems demand more artificial intelligent solutions to incorporate multiple fault events or dynamically changing load profiles in case of incomplete or noisy measurements [44; 45; 46; 47; 48; 49]. Commonly, the diagnosis and predictions are calculated through motor current signature analysis (MCSA) [50; 51], i.e., examining the output signals of the motor stator's current while running on a steady-state operating mood [52; 53; 54; 55; 56]. MCSA analyses the time-frequency decomposition of the current signals or by faults' frequencies in the frequency domain. MCSA works based on a single input source and representing a simple, low-cost and non-invasive monitoring method [50; 57; 58]. An enhanced method of MCSA in the case of multiphase electrical machines is called electrical signature analysis (ESA) [59]. A timely diagnosis of the complete rotating machinery system contributes to avoiding overpriced reparations and unexpected breakdowns. According to [60], the great majority of recent electric motor condition monitoring methods can be classified into three main categories. The first-class includes the detection of single faults by analyzing one or multiple parameters; the second class covers the detection of different faults with multiple parameters and processing techniques, and the last one contains the mixed techniques of various computing-intensive approaches to analyze different electrical and mechanical parameters in order to detect multiple faults [61; 62; 63; 64]. In contrast to conventional signal processing based fault detection techniques [65], recently a few attempts were made for the application of intelligent algorithms [66; 67] including new approaches to Fault Detection and Isolation (FDI) [68] based on fuzzy logic, decision trees, neural networks, and further machine learning techniques [69; 70; 71; 72; 73]. However, most of them rely on the measurement and processing of vibration signals, which require at least one vibration sensor, which demands extra costs for its proper installation and maintenance [74; 75; 76; 77]. In addition, a technician needs knowledge and a good amount of experience to correctly use such sensors [78; 79; 80; 81; 82; 83; 84]. However, the ESA reported being able to reveal a large number of relations between the machine parameters [85; 86; 87; 88].