# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 3 Theoretical Background
### Review of ML-Based Classification Algorithms  
As previously stated, recent AI advancements have aided in the improvement of ML and deep learning models for effective FDI, which typically involves relying on intelligent models for improved FDI at a minimal false alarm rate even amid uncertainties. Even though the efficiencies of these methodologies have been reported in numerous studies, there are still underlying challenges associated with their use, such as computational cost and their tendency to deviate from core engineering concepts, which makes them sometimes irrelevant for cost-conscious industrial applications, as presented in this study [32]. Traditional ML algorithms, on the other hand, offer a more cost-effective and dependable platform for adequate FDI because their efficiency is rarely affected by data availability [32; 33]. The effectiveness of FDI algorithms is highly dependent on the nature of the discriminative content of the input signal of the device under monitoring; thus, significant discriminative feature extraction from raw signals is critical [30]. As a result, this study was motivated to investigate various ML algorithms and their efficacy on current-based fault detection after peak-detection-based feature extraction. As a result, in this study, a handful of popular ML-based classifiers are presented and discussed to present their theoretical background for FDI. DT is one of the most common, cost-efficient, and reliable known ML algorithms that has been effectively employed for both regression and classification problems. DT is an algorithm that uses a tree-like structure of decision-making rules to classify input data into subsets and to make predictions based on this classification [34; 35]. Its two main advantages are its ease of use and its ability to present solutions with various outputs [35]. However, this model is prone to over-fitting and under-fitting, which can be overcome with pruning. Again, even with proper pruning, a perfect solution to the problem is not guaranteed [30]. Random forest (RF), on the other hand, mitigates the major challenges of DT by establishing a great number of decision trees at the same instance [30]. RF passes the presented sample through its various structures with different classifiers, computing and storing the output of each tree, which it further compares with single outputs of the popular trees to derive the final classifiers. By simply changing its key parameters, this model eliminates the major problem of DT [30]. One of the well-known disadvantages of RF is its complexity, which can result in high computational cost [36]. Booster algorithms such as Adaboost classifier (ABC), gradient boosting classifier (GBC), and XG boost (XGB) have been used to improve the efficiency and predictive accuracy of weak classifiers such as DT, regressors, and so on. These boosters are ensemble learning algorithms that combine weak learners to produce strong learners by minimizing their training errors [36]. However, these boosters have their challenges, which provides justification for further development of other algorithms to address such issues. For example, as more trees are added to their structure, these models are prone to over-fitting. However, in comparison, each booster presents a distinctive advantage over the other. GBC outperforms ABC in terms of accuracy due to its immense flexibility, which allows the algorithm as many differentiable and convex loss functions as possible [36]. On the other hand, XGB's scalability presents a structure that achieves algorithmic optimization, distinguishing it from the other boosters [37]. Interestingly, some ML algorithms make their predictions based on the assumption of a set of particular mathematical sequences or theories. For instance, k-nearest neighbor (KNN) is predicated on the assumption that any group of data with similar features will have similar feature values [38].