Predictive maintenance software is software that tells a factory or a plant which machine will fail, and roughly when, so that the repair can be planned before the line stops. The idea behind it is predictive maintenance. Instead of fixing a machine after it breaks, or replacing parts on a fixed calendar whether they are worn or not, the plant watches the machine and repairs it when its readings show a problem.
Plants want this because a surprise failure is expensive in two ways. The production line stops, and the plant loses output for every hour of the stop. An emergency repair also needs rush orders for spare parts and overtime for the repair team, which costs more than a repair booked in advance. The people who buy this software are usually the plant's maintenance and operations heads, and they judge it by the stoppages it prevents.
The older method is a fixed alarm limit, for example "alarm if the bearing passes 80 °C". By the time that alarm rings, the damage is often done. Predictive maintenance software instead learns what is normal for each machine and notices a slow drift long before any limit is crossed.
Say a fertiliser plant near Kakinada runs a cooling-water pump day and night. Sensors on the pump measure its vibration, temperature and electric current. An industrial IoT (IIoT) platform collects these readings and sends them to the cloud. An industrial data platform stores them next to the pump's maintenance records and drawings. Asset performance management (APM) software has learned what normal readings look like for this pump. When the vibration rises for several days, it raises an alert with the likely fault and a date.
The maintenance engineer then checks the fault. She can look at the pump in the plant digital twin, a digital copy of the plant that sensors keep up to date. Or she can send a drone or a robot with robotic inspection. The repair is then booked for the next planned shutdown.
The readings are a time series, a list of values recorded one after another with the time of each. Spotting a reading that has moved away from normal is called anomaly detection. Some industries, such as aircraft making, also need their design, parts and test records joined together. That is the job of engineering data integration. Increasingly, AI models explain the alert and suggest the fix, and the engineer approves it. This pattern is called human in the loop.