Industrial Sensor Anomaly Detection Using Machine Learning
تفاصيل العمل
Developed an end-to-end machine learning solution to detect anomalies in industrial sensor data for predictive maintenance applications. The project analyzes equipment operating conditions using sensor measurements such as temperature, vibration, and pressure to identify abnormal behavior before equipment failure occurs. The workflow included data preprocessing, exploratory data analysis (EDA), feature engineering, anomaly detection model development, model evaluation, and performance visualization. Multiple anomaly detection techniques were evaluated to improve detection accuracy while reducing false alarms. The solution demonstrates how machine learning can support predictive maintenance, reduce downtime, and improve operational efficiency in industrial environments.
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