The primary goal of this project is to develop an anomaly detection system for industrial equipment. The system's objective is to identify unusual behavior in equipment data and thereby prevent equipment failure, reduce downtime, and improve operational efficiency.
Data Generation/Preprocessing: Depending on the availability of real-world data, you may either generate synthetic data to simulate industrial equipment behavior or preprocess real sensor data. Data preprocessing may involve handling missing values, noise reduction, and data scaling.
Anomaly Detection Model: Develop an anomaly detection model capable of identifying deviations from normal equipment behavior. Common techniques include statistical methods, machine
Model Evaluation: Assess the performance of the anomaly detection model using appropriate evaluation metrics. Metrics such as precision, recall, F1-score, and receiver operating characteristic (ROC) curves can be used to evaluate how well the model identifies anomalies.
Visualization & Reporting: Visualize the results of the anomaly detection system and create reports that provide insights into unusual behavior patterns. Clear visualization and reporting can help stakeholders understand and act upon the detected anomalies effectively.
