Integrated Predictive Maintenance and Performance Optimization of BLDC Motors Using GA, PSO, and LSTM on Temperature and Vibration Data Targeting Bearing Defects

Brushless DC (BLDC) motors are critical components in modern industrial applications, prized for their efficiency and reliability. However, bearing defects remain a significant challenge, leading to performance degradation, increased maintenance costs, and unexpected downtime. Traditional fault detection methods, often relying on isolated temperature or vibration data, fall short in accurately identifying early-stage defects. This paper introduces a novel, integrated approach that synergizes Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Long Short-Term Memory (LSTM) networks to correlate temperature and vibration data for enhanced predictive maintenance and dynamic performance optimization. By leveraging the strengths of these advanced algorithms, our method significantly improves the accuracy of bearing defect detection while simultaneously enabling real-time optimization of motor parameters. We present a comprehensive theoretical framework, detailed methodological approach, and discuss the far-reaching implications of our model within the context of Industry 4.0. Our proposed system not only excels in early fault detection but also continuously adapts motor settings to optimize performance, promising substantial reductions in maintenance costs, marked improvements in operational reliability, and significant extensions to motor lifespan. This research represents a pivotal advancement in the field of industrial motor maintenance and sets a new standard for intelligent, predictive systems in manufacturing environments.