Predictive Maintenance – What you need to know
Here at Momenta Partners, we spend a lot of time speaking with innovators and end users about how to harness data from physical assets to optimise processes, reduce risk, grow businesses and even power brand new business models, says Ed Maguire, Insights partner.
Predictive Maintenance is top of mind across the board, with start-ups, professional service firms and established industrial firms seeking ways to realise benefits by “fixing things before they break” – potentially saving massive amounts of time, money and reputational risk.
What is Predictive Maintenance?
Predictive Maintenance involves simply being able to predict when a machine or part is likely to fail based on real-world data, then being able to take action to avert any problems. This is a simple concept, but difficult to realise. In industries like aerospace and transportation, being able to prevent failures can save lives.
While the recent Southwest Airlines fatality may not have been foreseeable, what if the data from the engine explosion could be used to prevent the next one? To be able to predict a potential failure, you need to start with data – historical data, particularly leading up to a failure – in order to create an algorithm that can flag signs of pending problems.
The path to Predictive Maintenance
Today industrial maintenance is mostly “preventive” in nature. Prevent Maintenance starts with a schedule. When firms purchase industrial or other assets, they typically follow recommended intervals for maintenance. Think about the recommendation to change the oil on a car every 3,000 miles. Outside of keeping to the prescribed schedules, most major repairs happen when there’s equipment failure, and assets themselves are replaced when they reach a certain age. Keeping to a schedule is useful, but it doesn’t provide an idea of what could happen in the future.
With the addition of Condition Monitoring, businesses can engage in Proactive Maintenance. Condition Monitoring involves tracking data in real-time – this could be data from temperature or vibration sensors for instance. This can help identify problems as they occur, and if there’s an unusual condition such as excess vibration or overly high temperature this can signal the need for proactive maintenance – a change of a part for instance.
Preventive Maintenance works from a predefined schedule, and Proactive Maintenance typically employs Condition Monitoring but neither approach will necessarily help a business anticipate what is likely to go wrong in the future.
To be able to predict failures, you need historical data to develop a predictive algorithm, preferably including periods leading up to and including failures or breakdowns. The involves collecting the data and performing analytics on the data, then testing, tuning and updating the algorithms with newly collected data. In this past this was an expensive process, that demanded highly specialised statisticians who could develop specialised models.
Once Predictive Maintenance solutions have been in place for some time, they can be couple with specific recommendations or instructions for the business to fix the problems before they occur. This concept, known as Predictive Maintenance, is the idea that analytics can identify pending problems, guide a […]