Not traditionally recognised as a glamourous component of facilities management: planned preventative maintenance is finally getting a makeover. More desirable than reactive maintenance due to the reduction in downtime and the improvement in workload planning, but PPMs still suffer a pessimistic portrayal – is this service needed? Can we skip this one? This filter still looks clean.
Our CTO, featured in UK journal FMJ last month, explains we need to be relying more on data to make better decisions to improve efficiency, cost savings and sustainability. “You can’t manage what you can’t measure”, after all. Read more from Paul McCarthy in FMJ here. So what alternatives do FMs have for asset maintenance and what data is needed?
What is planned preventative maintenance (PPM)?
Rather than waiting until assets break down and fix them reactively, a much better method is to schedule PPMs at regular intervals throughout the year. This could entail a visual inspection, a clean, lubrication, a filter change or part replacement, for example. Setting up PPMs using CMMS software is easy, and allows them to be combined with other works due at the same time and in the same area to make more effective use of team hours.
However, there is a risk of over-maintaining, which can shorten the life of the asset (such as over-lubricating a bearing in a pump), use up spares unnecessarily and waste the time of the engineer where the work was not required. These are generally risks worth taking, but FMs could also work towards a predictive maintenance strategy.
What is condition-based or predictive maintenance (PdM)?
Predictive maintenance uses sensors and indicators to predict when an asset is due for maintenance. For example, measuring the run time of an escalator and generating a service for xxx number of hours according to manufacturer guidelines; using a BMS to be alerted of readings outside of pre-established limits; or installing sensors on assets to monitor temperature, pressure, humidity, vibration frequency, and more. As these readings start to show deterioration or produce an abnormal result, then maintenance can be scheduled before any breakage occurs.
What is data-driven maintenance (DDM)?
Data-driven maintenance is a type of predictive maintenance but goes one step further. DDM uses information from multiple sources to intelligently determine when an asset needs maintaining. This could include maintenance history, previous breakages, downtime, and even other installations of the same asset elsewhere to identify operating patterns and predict when a service should happen.
In order to analyse this data, generative AI is required – something the industry as a whole has been reticent to jump on board with. And perhaps justifiably so? Budgets remain static but the expectation for FM productivity and transformation is through the roof – who can afford to implement every new gadget? But as Paul discusses with FMJ, AI can analyse data with such granularity to extract patterns and create actionable strategies that it really is a game-changer for FMs in terms of resource efficiency. And contrary to common perceptions, AI and IoT solutions are not costly and complex to implement, meaning FMs can start acting on insights quickly to drive meaningful operational gains.
For more information on types of planned and predictive maintenance, as well as our AI platform Senslinc, contact us here.
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