Enhancing Predictive Maintenance in Industrial Systems Through Acoustic Monitoring of Servo Motors Using Machine Learning (AI)

The reliability and efficiency of industrial machinery are pivotal in maintaining optimal production levels and minimizing operational costs. Predictive maintenance, as opposed to traditional reactive or time-based approaches, offers a proactive strategy for machinery upkeep, reducing downtime and extending equipment lifespan. This study introduces an innovative method for predictive maintenance focused on acoustic monitoring of servo motors, integrating advanced machine learning (ML) techniques to detect and diagnose potential failures before they escalate into costly repairs or operational halts. By employing an array of microphones, acoustic signatures of servo motors under various operating conditions were collected and analyzed. The data encompasses a wide range of normal operational sounds as well as those indicative of common faults. Utilizing a combination of feature extraction methods and ML algorithms, including convolutional neural networks (CNNs) and support vector machines (SVMs), the study achieves significant accuracy in classifying the condition of servo motors. This approach not only underscores the practical application of acoustic analysis and ML in industrial maintenance but also opens new avenues for the implementation of intelligent monitoring systems in various manufacturing environments. The findings suggest that acoustic monitoring, supported by sophisticated ML models, can serve as a powerful tool in the predictive maintenance toolkit, offering a scalable, non-invasive means of ensuring machinery health and operational efficiency.