Predictive Maintenance Model for Industrial Machinery

Create a predictive maintenance model that leverages historical sensor data from industrial machinery to forecast potential equipment failures. The model should utilize techniques such as machine learning, ARIMA, and anomaly detection to identify patterns and correlations in the data. Develop a comprehensive data pipeline to preprocess and analyze the sensor data, including data cleaning, feature engineering, and dimensionality reduction. Implement a decision-making algorithm that integrates with existing enterprise resource planning (ERP) systems to automate maintenance scheduling and reduce downtime. Provide a detailed report outlining the model's performance metrics, including accuracy, precision, and recall.