Time-Based Maintenance and Power Plants Operation
Whether you are a manufacturing company or energy provider, you know one thing. Time-based maintenance is both your obligation and regular activity. You schedule it for crucial inspections and part replacements. All that to keep your production line efficient and safe. Time-based maintenance is not only about its final results. It’s also about doing it the right way. How? For instance by choosing its optimal time, hence minimizing the costs.
For Switzerland’s major energy provider, operating 3 nuclear power plants and 30+ hydropower plants, one clever decision led to yearly savings of 18+ million CHF . The decision? Use Datali’s help to develop a predictive maintenance assistant. The tool that suggests the best maintenance timeslots based on their expected costs.
Inside the Predictive Maintenance Assistant
The costs of time-based maintenance are influenced by several factors. For energy providers, it is the changing energy price that matters the most. Here even the weekday or the right hour that maintenance work begins with can be a game-changer. For hydropower plants, like run-of-river power plants, one should also take into account environmental factors like the water level.
We focused on combining forecast models with optimization. We designed and developed several time-optimization models. Each one focused on specific factors influencing the maintenance costs of the given power plant type. Based on historical data and developed forecasts, it proposed optimal time slots for maintenance work. However, it wasn’t the end.
The True Assistant for Experts
During the talks with subject matter experts (SME), it turned out that factors contributing to the final date choice are quite limitless. Few constraints were obvious almost immediately. On the other side, including hundreds of other factors would have taken months to implement. Include them all? It’s a matter of even years.
That’s why we took another, more human-centric approach.
Our predictive maintenance assistant helps you find the optimal maintenance date, based on any given starting date. It provided the very-needed flexibility. The user provides a candidate date and the flexibility window - by how many days can the date by shifted. The additional parameters included marking if the specific maintenance work has to be continuous and how long should it last.
The result? For planners: List of possible dates, together with their savings potential. For the business: highly significant savings.
Wonder what a similar solution would do for you and your industry?
Datali’s here to help. Just reach us out for more details!
