###### Abstract  
Industrial belt failures pose significant challenges to manufacturing operations, often resulting in costly downtime and maintenance interventions. This study presents a comprehensive approach to belt failure analysis, leveraging advanced monitoring and diagnostic techniques. Through the integration of motor current signature analysis (MCSA) and machine learning algorithms, particularly long short-term memory (LSTM) networks, this study aims to predict and detect belt degradation in real time. The methodology involves the collection and pre-processing of raw spectral data from industrial assets, followed by the training and optimization of predictive models. The effectiveness of the approach is demonstrated through extensive testing against real-world data, showcasing its ability to accurately forecast belt failures and enable proactive maintenance strategies. The results obtained from the testing phase reveal a high level of accuracy in predicting belt failures, with the developed models consistently outperforming traditional methods. The incorporation of LSTM networks and swarm intelligence algorithms led to a significant improvement in predictive capabilities, allowing for the early detection of degradation patterns and timely intervention. By harnessing the power of data-driven predictive analytics, the research offers a promising pathway towards enhancing operational efficiency and minimizing unplanned downtime in industrial settings. This study not only contributes to the field of predictive maintenance but also underscores the transformative potential of advanced monitoring technologies in optimizing asset reliability and performance. b 2025 13 June 2025 18 June 2025 19 June 2025 19 June 2025 19 June 2025 16 May 2025 16 May 2025 13 June 2025 18 June 2025 18 June 2025 18 June 2025 18 June 2025 19 June 2025 19 June 2025 17 July 202  
Structurally, an induction motor comprises the following two main components: the stator and the rotor. The stator, energized by an alternating current (AC), generates a rotating magnetic field. This field induces an electric current in the rotor, which in turn creates a secondary magnetic field. The interaction between these fields produces torque, causing the rotor to rotate and provide mechanical power to external systems [3,4]. Despite their robustness, induction motors remain susceptible to faults that can compromise operational continuity. Among these, belt failures are particularly frequent in motor-driven systems that rely on belt-pulley mechanisms for power transmission [5,6]. Such failures can lead to mechanical unbalances, decreased efficiency, unscheduled downtime, and increased maintenance costs. Belt degradation manifests itself in several forms, including misalignment, slippage, tension loss, fraying, cracking, and material fatigue, often results from prolonged operation, harsh environments, or improper setup [7,8]. Given the critical role that belts play, the early detection and prediction of belt-related faults have become a priority in industrial maintenance. Advances in sensing technologies and data analytics have enabled the transition from reactive to predictive maintenance strategies. In particular, the use of machine learning techniques has shown promise in forecasting equipment degradation and preventing failures before they occur. Among these techniques, artificial neural networks (ANNs)--especially deep learning architectures such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks--have demonstrated strong performance in modeling time-dependent patterns within complex, non-linear, and noisy datasets [9]. These models eliminate the need for extensive manual feature extraction and have been successfully applied in various prognostic applications. However, most existing approaches target general motor faults or rely on processed vibration signals, often neglecting the potential of electrical data to identify mechanical issues such as belt wear. This study aims to address this gap.