# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 2 Motivation and Literature Review
Their proposed methodology used an FFT algorithm to interpret current signals for reliable anomaly detection in the IM. The authors also used advanced signal processing techniques in their proposed method for critical FDI at the bearing and rotor bar of SCIMs to improve the interpretation of current signals. [PERSON] et al. [6], trained features extracted from the FFT spectrum of raw current signals using a machine learning algorithm. According to their research, their methodology presents a low-cost, accurate, and robust FDI instrument for SCIMs that uses only current signals and is also applicable to real-world data. In [17], the authors used a novel methodology that combined two techniques, wavelet and power-spectral-density (PSD), to analyze the FFT spectrum of MCSA. The technique's effectiveness was been demonstrated in diagnosing short turns and broken rotor bars in non-constant-load-torque SCIM applications, just as it does in constant-load-torque motor applications. In [8], [PERSON] proposed a fault detection algorithm for SCIMs using FFT and PCA. He employed FFT to analyze induction motor current in the frequency domain for fault-characteristic spectral components and used PCA for easy extraction of features from the available components. In reality, SCIMs and induction motors are often operated under varying loads. It is not recommended to meet/exceed the motor's maximum loading specification; however, production demands may sometimes require increased loading. These situations often induce stress and/or faults in motors, which may gradually evolve into failures [19]. On the flip side, SCIMs also experience faults that are humanly undetectable under low-load conditions [20]. At low-load conditions, SCIMs often do not generate noise and/or observable fault symptoms, which makes operation riskier since abrupt failures may occur, leading to interrupted production and/or accidents. This rationale makes it necessary to develop intelligent monitoring for early fault detection in SCIMs at low-load conditions. [PERSON] et al. [20] exploited the Hilbert transform (HT) and a neural network for broken rotor bar intelligent diagnosis in induction machines at low load. Beyond the efficiencies and limitations of their stand-alone HT-based diagnostic technique, other signal processing techniques could be integrated for comprehensive fault diagnostic efficiencies. For instance, [PERSON] et al. used an extended Park's vector approach in conjunction with FFT, DWT, and PSD to process and extract features from current signals for distinguishing induction motor inter-turn stator winding faults from unstable supply voltage conditions [21]. The coefficient of the major peak observed in FFT was used in their methodology to indicate fault severity and load level in the IM. [PERSON] et al. (2018) proposed a method for implementing and analyzing current signatures from a three-phase SCIM using a combination of three signal processing techniques: FFT, short-time Fourier transform, and continuous wavelet transform. Their method demonstrated that MCSA can detect changes in frequency components by obtaining the FFT-based spectrum that contains the initial information about the fault. In [PERSON] et al. (2018), the authors demonstrated the non-intrusive nature and simplicity of MCSA. They presented a methodology for detecting faults in an SCIM's stator winding using external flux sensors on a three-phase SCIM. The sensors were placed on the outside of the motor's body in the X, Y, and Z directions so as not to interfere with the motor's operation. FFT analysis of the stator currents revealed a short circuit fault in the SCIM stator winding. For all these successful case studies, one major take-home is the high efficiency of FTs for MCSA and its robustness for isolating fault frequencies on frequency bands in varying magnitudes. This provides a reliable avenue for harnessing the information provided by frequency-magnitude coordinates as representative features for discriminate modeling for FDI.