# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 6 Discussion, Open Issues, and Future Work  
Despite the robustness and low cost of maintenance for extended use of SCIMs [1], the increasing industrial reliance on these power machines, with the critical need to assess for efficiency, reliability, and safety, as emphasized by this study's goals, cannot be overstated. This study focuses on presenting an ML model that can provide a high level of fault classification performance for various types of faults that are common in SCIMs; nevertheless, we consider the following to be some of the study's inadequacies: Although the findings revealed the cost efficiency and accuracy of the proposed peak-based feature extraction and ML-based diagnosis, the need for improved diagnostic performance is still critical considering that against the limitations of traditional ML-based algorithms (accompanied with hand-crafted feature extraction processes), the superior feature extraction and classification efficiencies of deep learning models have been shown in multiple studies. These deep learning models--convolutional neural networks, recurrent neural networks, etc.--are popular for automated feature learning; however, their mystical defiance from the statistical principles from which they were fundamentally designed has been a major issue for their adoption. In addition, other issues such as trustworthiness, computational cost, increased complexity, over-fitting/under-fitting, high stochasticity in learning, extensive parameterization/optimization, etc., contribute significantly to hesitation towards generalization. On the other hand, although the traditional, hand-crafted feature extraction process for ML-based diagnosis offers comparatively poorer diagnostic efficiencies in relation to deep learning models, it does offer a transparent architecture for ensuring explainability, empirical investigations, and trustworthiness. Under realistic situations, the occurrence of the SCIM failure modes presented in this study are not mutually exclusive and may occur in little or intense degrees. This presents the issue of accurately identifying the fault type (or combination of fault types) in place. For example, turn-to-turn short circuit may be minimal (just two turns), intense (several turns in the same phase), or may become very intense when it grows to a phase-to-phase short circuit. Although this presents a broad opportunity for more extensive research, it becomes an endless uphill task to replicate all the possible failure modes, their individual degrees of severity, the possible failure combinations, and their respective degrees of combined severity. Nonetheless, our study offers a reliable feature extraction approach that is expected to direct continued research in the domain. On a different note, the frequency-domain approach for SCIM fault diagnosis has some limitations, which are mostly inherited from spectral leakage and lack of transient information. The models often perform poorly in finite-time window situations, require high-frequency resolution for adequate performance, and exhibit unstable variations in side-band frequencies in varying load situations. In addition, they are often flawed at detecting certain faults at no load conditions, especially the broken rotor bar [21; 45]. On the other hand, deep learning and time-frequency domain signal processing techniques offer better discriminative feature extraction efficiencies; however, they are often associated with high computational costs. Because current signals are often stationary and do not often exhibit any transient changes, we believe the proposed frequency-domain approach is more beneficial, considering that the efficiencies of its counterparts--time-domain, time-frequency-domain approaches, and deep learning methods--are insignificant, computationally expensive, and highly unexplainable, respectively. At minimal load conditions (as presented in our study), the peak detection technique proposed herein offers reliable discrimininative efficiencies.