Introduction Predictive maintenance is a crucial aspect of industrial automation, allowing companies to reduce downtime, increase efficiency, and save costs. Traditional methods of predictive maintenance rely on manual inspections and scheduled maintenance, which can be time-consuming and ineffective. With the advent of artificial intelligence (AI) and machine learning (ML), it is now possible to build custom AI agents that can predict equipment failures and schedule maintenance accordingly. In this article, we will explore how to build a custom AI agent for predictive maintenance in industrial settings using Python and TensorFlow. Background Predictive maintenance involves analyzing data from sensors and machines to predict when equipment is likely to fail. This requires a vast amount of data, which can be collected from various sources such as temperature sensors, vibration sensors, and pressure sensors.…