# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 1 Introduction  
SCIMs are used to power most industrial appliances because of their robust nature and ability to generate sufficient torque to effectively drive much larger machinery at an affordable cost through the process of electromagnetic induction. Injection sea water pumps, air conditioner compressor drives, gas circulators in power generating firms, and oil exporting pumps in the oil and gas drilling industries are only a few of the well-known applications of SCIMs [1]. SCIMs are often prone to failures and breakdowns as a result of faults and prolonged operation, and, if left unmonitored, often suffer major damage or breakdowns. According to a review done by [PERSON] et al. [2], severe operating environments, insufficient insulation, purposely overloading the power supply, and factory defects are the most typical causes of breakdowns and failure. The production down-times caused by these flaws have frequently resulted in revenue loss, among other pitfalls. Therefore, early fault detection is important/crucial to avoid these occurrences [1]. The consequences of these failures have increased the need for SCIM failure diagnosis as a crucial module for overall equipment prognostics and health management (PHM).  
State-of-the-art research studies on SCIM FDI and prognostics feature data-driven PHM technologies, with research ongoing; these studies show that data-driven AI-basedPHM technologies rely heavily on the quantity and quality of data to train AI-based predictive modeling [1,3]. However, the accuracy of these models is also dependent on how suitable the method befits the nature of the available data, which has led to various studies exploring numerous data-driven PHM methodologies. Researchers have used advances in artificial intelligence (AI), machine learning (ML), and deep learning to construct models that exploit current signals, vibration signals, and thermal signals generated by equipment via sensors, examining these signals separately or combining them for FDI [1,4,5]. Fourier analysis has proven to be effective among the different ways of analysis owing to its convenience and nature of application, especially when it comes to current signature analysis and/or vibration signature analysis (VSA) [5]. Fourier transforms (FTs) are essentially concerned with the decomposition of signals from their time domain to their frequency domain for analysis in both healthy and faulty motors, providing a superior platform for signal interpretation and feature extraction for FDI [3,6]. Even though it can decompose signals to their frequency domains, a FT still has limitations, such as its lack of transient information and its nature of providing only the average time of the spectrum content, thereby lacking in providing details on variations in frequency with regard to time of the signals [7]. Fast Fourier transforms with high computation speed and short-time Fourier transforms that decompose data into the time-frequency domain are frequently used to solve these challenges [7,8]. Further, according to [9], Fourier analysis is one of the highly efficient analytical tools that is compatible with MCSA for a variety of fault detection for SCIMs.  
Although variable frequency drives (VFDs) have recently become more popular in industries than direct online starters due to their ability to provide flexible production control and soft motor start-up, variable frequencies, complex control systems, and harmonics generated at the drive output are still some of the major concerns they are associated with [10]. Harmonics generated by VFDs pose a significant problem for motor bearings and stator windings since they raise their level of stress. Moreover, they have an impact on signal quality in terms of noise ratio, particularly when using stator current signals for FDI [5,10].