In many cases, current best practice is to employ predictive maintenance in combination with more sparsely scheduled preventative maintenance. This allows higher component utilisation, along with reduced downtime, while still making use of inspections to ensure reliable operation. Maximum lifespan of equipment under this method is often the greatest, as wear and tear of components is usually isolated before affecting adjacent areas of the system.
There are many exceptions to these rules, with two that stand out in particular. The first is that small, low-cost, and isolated systems are often easier to replace than maintain. The second is that large operating equipment with long downtime periods will often find value in scheduling full inspection periods during necessary downtime intervals. This still falls within the hybrid model, but is more of a “preventative” approach that moves further away from just-in-time practices.
How do I deploy predictive maintenance for my system?
There are two main factors to consider:
1. Stages of complexity
2. Stages of deployment
- To start with, you need to begin collecting and storing live data - as much of it as possible, and as early as possible.
- The next step is to identify thresholds for each signal. These should alert you if expected operating values are being exceeded.
- To improve on this, anomaly detection models can be deployed to recognise unusual patterns of behaviour across different signal groups and time ranges (for more detail see our post on anomaly detection).
- To start making predictions further up the PF curve, the next improvement generally comes from training supervised learning models (to be discussed in a future post) to predict failure cases. This requires labelling either historic cases of failure or simulated cases of failure and generally requires more engineering effort. However, once trained, this can be a powerful tool to employ across your assets.
- We usually recommend that you begin with your standard baseline schedule for maintenance.
- Once you begin rolling out predictive maintenance models, note down any reductions in reactive maintenance, and then begin to increase the time between relevant scheduled replacements. Depending on the risk involved, you can also substitute scheduled replacements with scheduled component inspections.
- As confidence increases in the predictive maintenance models and their sophistication increases with more data, learning ability, and faster algorithms, preventative maintenance can be phased out over time.
If you would like to talk about implementing predictive maintenance models for your plant or system, or if would simply like to begin consolidating your equipment data in preparation for such models, please contact us at email@example.com.