To effectively leverage AI for predictive maintenance in manufacturing, it is crucial to collect quality data for analysis. Start by ensuring that all relevant sensors and monitoring systems are properly installed and calibrated on the machinery. These sensors should capture key parameters such as temperature, vibration, performance metrics, and maintenance records. Regularly retrieve and store this data in a centralized database or cloud platform for easy access and analysis. Additionally, consider integrating data from other sources, such as historical maintenance logs or external environmental factors, to gain a comprehensive understanding of the equipment’s condition. Remember, the accuracy and reliability of the insights generated by AI algorithms heavily rely on the quality and quantity of the data collected, so invest time and effort into ensuring a robust data collection process.