Advancing Machine Learning-Based IoT Sensor Data Processing for Industrial Predictive Maintenance Applications

Predictive Maintenance (PdM) solutions are revolutionizing industrial operations because to the integration of Internet of Things (IoT) and Machine Learning (ML). With the use of Internet of Things (IoT) sensors, these systems can track machinery health and identify when it might break down, which in turn improves operating efficiency and decreases downtime. Processing, interpreting, and making decisions in real-time are all made much more difficult by the massive amounts of diverse data produced by IoT devices. Modern predictive maintenance relies heavily on machine learning since it provides strong solutions for processing such complicated data. The article delves into recent developments in processing data from Internet of Things (IoT) sensors using ML for predictive maintenance in commercial settings. With an emphasis on supervised, unsupervised, and deep learning methods, it provides a synopsis of current research in processing sensor data. Data preprocessing and dimensionality reduction techniques are among the options proposed in the paper as means to enhance model accuracy and efficiency while dealing with noisy, high-dimensional IoT data. The article also emphasizes how edge computing helps with real-time data processing, which makes PdM systems even more scalable. This work's main contribution is an in-depth analysis of machine learning (ML) models used to Internet of Things (IoT) data for RUL estimation, anomaly detection, and failure prediction. Particularly in the creation of hybrid ML models and interpretable AI for use in industry, the study also details forthcoming trends and unanswered questions. Industries like transportation, energy, and manufacturing rely on these innovations to optimize maintenance procedures, increase equipment reliability, and save operational costs.