Predictive Maintenance

Manufacturing maintenance has three stages: reactive maintenance (fix after failure), preventive maintenance (inspect on schedule), and predictive maintenance (forecast failures based on data). Predictive maintenance monitors actual equipment conditions, reducing unnecessary scheduled replacements while preventing unexpected shutdowns.

The mechanism is straightforward. Time-series data is collected from vibration sensors, temperature sensors, and ammeters. A machine learning model learns normal operational patterns. When live data starts deviating from these patterns, it triggers an alert as a sign of impending failure. Isolation Forest and autoencoders are commonly used for anomaly detection.

The impact is significant—directly reducing downtime, optimizing parts inventory, and improving worker safety. In manufacturing facilities across Thailand and Laos, it draws attention as an approach to compensate for the shortage of experienced technicians through AI.

However, sensor installation costs and the data accumulation period (at minimum 3-6 months of normal operational data) can be barriers. Starting small with the single piece of equipment that has the highest downtime cost is the most realistic approach.