Problem Statement:
In industrial environments, unexpected equipment failures lead to costly downtimes and production losses. Anomaly detection in equipment sensor data can help predict these failures before they occur, enabling proactive maintenance. This project aims to use machine learning algorithms to detect abnormal patterns in sensor data, allowing operators to take preventive actions. Solution:
Develop and deploy a machine learning based anomaly detection system using algorithms such as Isolation Forest , Support Vector Machines (SVM) , Random Forest , XGBoost , and LightGBM to classify normal and abnormal sensor data. These models will predict potential failures by detecting anomalies in the behavior of the equipment, minimizing unplanned downtime.