When it comes to asset management, there is an evolution happening within the energy industry where utilities are moving from preventative maintenance to predictive maintenance.
Historically, utility companies have utilized preventative maintenance tactics to maintain the electric grid reliability, which involves following a predetermined fixed schedule to regularly inspect and service assets. Predictive maintenance, on the other hand, is not dictated by a predetermined schedule; rather, targeted areas for maintenance are dictated based on predictive analytics.
The Nuts and Bolts of Predictive Maintenance
Predictive maintenance utilizes algorithms and machine learning or artificial intelligence to anticipate when an asset will need maintenance or servicing. Obviously, this is not a pencil and paper exercise – the idea relies on complex software that crunches numbers essentially in real time.
The idea relies on benchmarking as well – comparing the asset’s age, previous failure rates, local conditions, and other data points to industry averages. The bottom line is that predictive analytics or maintenance relies on real time intelligence and analysis rather than a calendar, and moving away from preventative maintenance and toward predictive maintenance can generate substantial cost savings, as shown in this article.
Unfortunately, utility adoption levels for predictive analytics remains low, mainly due to the inertia associated with the desire to avoid costly, complex and widespread operational changes. The concept requires a huge investment in hardware, software, system integration, and employee training. The good news is that this predictive capability can be outsourced to companies such as SAP and IBM.
There is little doubt that predictive maintenance is the wave of the future. The only question in my mind is, when will the future be now?