Predictive Analysis Development Using Machine Learning for Industrial Equipments

The project titled “Predictive Analysis Development Using Machine Learning for Industrial Equipment” aims to improve predictive maintenance (PdM) by utilizing machine learning (ML) models to foresee equipment failures before they happen. By examining realtime sensor data such as temperature, vibration, and pressure, the goal is to transition maintenance strategies from a reactive to a proactive stance. Various machine learning models, including Random Forest, Logistic Regression, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), are employed to forecast failures, while unsupervised methods like Isolation Forest and Autoencoders are used to identify anomalies. Combining these techniques enhances the accuracy of predicting both frequent and infrequent failures, which in turn reduces downtime and maintenance expenses. This proactive strategy boosts operational efficiency by facilitating timely interventions, optimizing maintenance schedules, and lowering repair costs, marking a significant advancement in the management of industrial equipment.