# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 2 Motivation and Literature Review  
As dependency on SCIMs by industries is on the increase due to their robust nature, failures of these machines cannot be accommodated; therefore, the need to find solutions to these failures or to predict possible failure time cannot be overemphasized. Globally, close to 90% of industrial equipment relies on SCIMs as their prime mover [11]. According to [PERSON] et al. [1], faults in SCIMs are categorized based on the location of occurrence, i.e., internal or external, and these faults are then grouped based on the nature and/or origin of the fault, i.e., mechanical or electrical faults; the severity of each fault type depends on its location. For instance, an external electrical fault is less threatening than an internal electrical issue, but if left unmonitored, it can escalate to an internal electrical fault, which can lead to a total breakdown. Bearing and stator faults account for more than 70% of general failures in SCIMs [12]. According to an investigative report, bearing failure, unsurprisingly, has the highest percentage of occurrence in an SCIM (40-45% contribution) [1], which can be traced to the nature of SCIMs, whose bearings are susceptible to damage when overloaded, misaligned, and/or unbalanced. Moreover, the bearings are subjected to continuous loading at all times when the SCIM is in operation. Stator failures are common as well, owing to large current flows in their winding coils and insulation weakness caused by mechanical and electrical stress and/or deterioration of insulation. The most common defect in a stator is an inter-turn fault, which happens when two turns in a phase become linked as a result of failed insulation and, if left unchecked, can lead to phase damage or more serious stator failures, resulting in substantial maintenance costs [13]. Although not as common as stator and bearing problems, rotor faults are one of the most commonly occurring faults, accounting for over 8% of all SCIM faults [1], and can lead to poor performance and/or breakdown of the motor if left unmonitored. For adequate FDI, signals generated from these machines are employed for condition-based monitoring, which has proven to be useful in past and present research studies [5; 14; 15]. Based on the nature of SCIMs, which generate vibration and thermal responses while in operation, vibration signals have been one of the most-used measurements thus far due to their efficiency in both time and frequency domains [16]. On the flip side, due to the simplicity, low cost, and non-intrusive nature of obtaining current signals, MCSA has become very popular in recent times. This can be linked to the unique current signatures from the IM's supply [5; 15]. Due to the effectiveness/efficiency of this approach, MCSA has been effectively employed in both stator and rotor monitoring [1; 11]. Furthermore, MCSA has also proven to be effective and compatible with Fourier analysis for discriminant feature extraction for critical FDI [6; 8; 12; 14; 17]. In the literature, various studies have been presented for fault diagnosis frameworks and classifications that exploit MCSA and spectral features extracted based on Fourier series transformations of signals from their time to frequency domains. For instance, in [15], the authors used both MCSA and FFT under different conditions. Two faults were considered in the study to evaluate their proposed algorithm using stationary and non-stationary signals. Studies presented in [5; 14] employed MCSA for SCIM fault detection in broken rotor bars. In their methodologies, spectral features obtained from applying FFT analysis to MCSA were applied to independent component analysis (ICA) for improved performance; the extracted features were labeled FFT-ICA and were proven to contain a wealth of information for FDI with good outcomes when used for online fault detection. In [18], the authors capitalized on the effectiveness of current FDI monitoring.