## 1 Introduction
### Current Signature-Based Fault Diagnosis  
While vibration analysis is a well-established technique for detecting faults, specifically mechanical faults, it presents several limitations for industrial applications [4]. Vibration sensors are often expensive, require precise installation, and are susceptible to environmental noise, which adversely affects the performance of fault diagnosis techniques [17]. Additionally, accessing motor bearings or the motor itself for sensor placement or even external attachments can be challenging in certain operational environments. In contrast,current signature analysis (CSA), which can be easily captured at any point along the cables, offers a more cost-effective and non-intrusive solution for the fault diagnosis of IMs. The application of CSA for IM fault diagnosis has been extensively studied, with various techniques developed to enhance fault detection, classification, and location [18]. In [5], a model based on empirical mode decomposition (EMD), multi-resolution analysis (MRA), and fast Fourier transform (FFT) was proposed to extract 144 features from current signals. A feature selection process combining symmetrical uncertainty (SU), binary grey wolf optimization (BGWO), and emperor penguin optimizer (EPO) was employed and achieved an accuracy of 99.54% on the Case Western Reserve University (CWRU) benchmark and 99.52% on the machinery failure prevention technology (MFPT) dataset. Similarly, the Hilbert-Huang transform (HHT) was applied to extract features in [19], and the extracted features were ranked using a feature selection based on distance discriminant (FSDD). Combined with a backpropagation neural network (BPNN) algorithm, the proposed technique reported an accuracy of 98.98%. However, the high false-negative rate (13.66%) suggests that further refinement is needed for more precise fault detection. The synergetic combination of particle swarm optimization (PSO) and BPNN was presented in [20]. The proposed approach initially extracted 50 features and reduced them to nine via a mean impact value (MIV) algorithm. The model maintained an accuracy of 99.4% while reducing computational time by 9%. However, the use of 50 hidden layers increased the risk of overfitting. In [21], a hybrid genetic binary chicken swarm optimization (HGBCSO) algorithm was adopted for feature selection. The model used local mean decomposition (LMD) and wavelet packet decomposition (WPD) to reduce data dimensions and extract essential features. On the other hand, a practical edge-computing approach for motor fault diagnosis using CSA was also presented in [22]. This method utilized the affordable Arduino platform for data acquisition, signal processing, and model deployment, with a focus on real-world implementation. The model produced positive results when tested on electrical machines. A different approach was introduced in [23], which utilized singular analysis to detect faults in stator current signals. The method decomposed the current time series into singular triples using Hankelization matrices. However, the complexity and computational demands of the singular analysis and matrix Hankelization processes limit its practicality for real-world industrial applications. Additionally, recent research that emphasizes the role of AI and deep learning techniques in fault diagnosis is reviewed in [24; 25], and [7; 26; 27], respectively. Alternatively, feature reduction techniques offer an attractive solution to eliminate the curse of dimensionality. Thus, [28] proposed a fault classification technique using radial basis function neural networks (RBFNNs) and probabilistic neural networks (PNNs), incorporating principal component analysis (PCA) for feature reduction. Similarly, [29] employed receiver operating characteristic (ROC) curves and t-distribution stochastic neighbor embedding (t-SNE) for dimensionality reduction, improving classification accuracy by 2-3% and reducing data dimensions by 70%.