Next maintenance stoppage – Mileage, predictivity or something else?
During planned maintenance stoppage, companies have different strategies servicing and replacing machinery components, for example:
• Time-based using component manuals and engineering experience
• Predictivity combining data and engineering knowledge
Practices can scale from the more basic reactive maintenance (act when something has happened) to more advanced predictive maintenance, which offers real-time / online data to allow informed decision-making and ideal timing of the stoppage.
Run-to-failure is based on fixing the equipment only after it breaks down. While it doesn’t require actions or efforts beforehand, and might seem cost-effective in the short term, it can lead to unplanned downtime, reduced productivity, and increased repair costs. Run-to-failure can be considered sufficient option when the machinery is non-critical, or the cost of failure is low.
Time- or often machine-operating-based practice involves scheduled inspections and maintenance tasks to prevent potential failures. While it's a clear step-up from run-to-failure-method, it still has its disadvantages due to potential unnecessary downtime if equipment is replaced or repaired before it's necessary. Time-based practice can be considered suitable for equipment with known failure patterns or components with predictable life cycles.
Predictivity-based practice monitors machinery health in real time. This approach combines the power of predictive data with the expertise of engineering team for a precise and optimal time of maintenance stoppage to continue the production with minimal downtime while reducing maintenance costs and maximizing operational efficiency.
The way forward, combining engineering and data.
APL Systems Head of Acoustic Analysis, Roy Hjort comments on the importance of good cooperation between customer and APL: “While predictive data is important part of keeping the production flowing, the cooperation really comes into fruition when you combine online measurements with customer’s engineering knowledge. For example, by daily tracking frequency trends, looming problems can be noticed and evaluated before any signs of problems. This can then involve implementing short spot measurements together with customer to find out if the risen trends are harmful in the long run. So really, it’s all about good cooperation and both sides communicating with each other to achieve the best result.“