There is considerable excitement about the potential of advanced predictive maintenance approaches.
The promise of these new techniques is tantalizing. Using machine learning technologies to analyze historical performance and failure data, they aim to forecast when and how a component is likely to go wrong in the future with a high level of predictability.
Several companies have started the journey by setting up automation and instrumentation, which combined with rigorous maintenance record keeping will create the rich data that machine learning systems require.
Although it seems like an easy endeavor, there are things that asset managers need to consider:
- Data. Unplanned downtime might be concentrated in a small number of large events. That means you will have too few datapoints for Predictive Maintenance systems to learn from.
- Time. Predictive models work over time horizons that might be too short to be useful. Predicting that a part will fail in two days or two weeks is useful in a truck or machine tool, but it may not help in a plant where shutdowns take several days, and maintenance teams require months to plan interventions and source spare parts.
- Impact. Facilities operate critical assets with a high degree of redundancy and few single points of failure. If a pump stops unexpectedly, operators can often switch to a backup unit with little impact on production making the impact from Predictive Maintenance low.
Request for a session with our experts to learn how Maximo can help to facilitate your Journey to Predictive Maintenance.