Overview
Predictive maintenance (PdM) is maintenance that monitors performance and condition of equipment during normal operation to reduce a likelihood of failures. Also known as condition-based maintenance, predictive maintenance has been utilized in the industrial world since the 1990s.
Predictive maintenance evaluates the condition of equipment by performing periodic (offline) or continuous (online) equipment condition monitoring. The ultimate goal of the approach is an ability to predict when equipment failure could occur (based on certain factors), and to perform maintenance at a scheduled point in time prior to this, i.e. when the maintenance activity is most cost-effective and before the equipment loses performance (within a threshold).
This results in a reduction in unplanned downtime costs because of a failure where, for instance, costs can be up to hundreds of thousands per day depending on industry. This is in contrast to time- and/or operation count-based maintenance, where a piece of equipment gets maintained whether it needs it or not. Time-based maintenance is labor intensive, ineffective in identifying problems that develop between scheduled inspections, and so is not cost-effective. So, the fundamental idea is to transform the traditional “fail and fix” maintenance practice to a “predict and prevent” approach. Predictive maintenance differs from preventive maintenance because it relies on the actual condition of equipment, rather than average or expected life statistics, to predict when maintenance will be required.
Some of the main components that are necessary for implementing predictive maintenance are data collection and preprocessing, early fault detection, time to failure prediction, maintenance scheduling and resource optimization through "just-in-time" in manufacturing.
Modern technologies based on integration of Artificial Intelligence (AI) and Machine Learning (ML), allow us to solve most of such problems in an effective way. They include learning from time series of historical data with known healthy periods of operation and also time points when certain failures occurred. Using this data one can train models that would be able to detect anomalies in data and predict probability of a failure.
