What is Predictive Maintenance (PdM)?
Predictive maintenance, or PdM, is a new approach to maintenance management that’s a subset of predictive analytics. It uses data from IoT sensors, artificial intelligence (AI), and machine learning (ML) together to direct maintenance efforts towards the parts that need it most.
Predictive maintenance is more efficient than reactive maintenance or preventive maintenance, which process plants traditionally used. Instead of following a rigid maintenance schedule or waiting until a part shows visible signs of failure, predictive maintenance picks up on the earliest hints of problems to build a flexible, ever-changing maintenance schedule.
Why does predictive maintenance matter to process plant engineers/process plants/process manufacturers?
If one of the many parts in a process plant suddenly fails, production could grind to a halt while the failure is investigated and addressed.
Maintenance teams try to avoid this by checking parts on a regular basis, but it’s impossible to predict when a part might fail. Predictive maintenance helps you streamline maintenance schedules to include the parts that need it most, and save time and effort investigating parts that don’t yet need inspection.
Predictive maintenance gives you a much earlier warning about possible part failures, so you won’t be taken by surprise. This way, you can:
- Schedule repairs for the most convenient time
- Catch a part before it fails and repair it, instead of replacing it
- Order replacement parts in time to prevent extended shutdown
- Save on maintenance cost
What are the best ways to implement predictive maintenance?
Keep expectations realistic
Unrealistic expectations sometimes arise around predictive maintenance solutions, with the overly optimistic hope to receive maintenance or replacement schedules months in advance. Since predictive maintenance relies on a continuous feed of real-time data, it typically anticipates problems several weeks early. This still gives engineers a chance to investigate and prevent the failure, and often uncovers challenges that would not have been considered months earlier.
Encourage a culture change
Advanced predictive maintenance solutions pick up on slight anomalies that are the first signs of impending failure. If your process engineers are used to a “run to fail” mentality, they’re liable to dismiss early alerts as insignificant. You need a culture change that assumes that you can extend equipment life cycles and that replacing minor parts regularly isn’t inevitable.
Lead from the top
Implementing predictive maintenance requires strong, consistent leadership that carries through a paradigm shift. Process plant managers need to motivate employees to master the new culture of predictive management through leading by example and persistent education.
Appoint a dedicated team
The best way to quickly see value from your predictive maintenance solution is appoint an individual or a team to take responsibility for investigating alerts on a regular basis. This way, they’ll learn how to master the solution more swiftly, and help prove value to encourage widespread adoption.
How does predictive maintenance make process plants more productive?
When you implement predictive maintenance, you can save money on equipment lifecycle, prevent unexpected or extended shutdowns, and make better budgeting decisions across the plant. Plants that use predictive maintenance carry out repairs faster and for lower costs, often avoiding expensive, frequent replacements.