For many years, the maintenance of everything from plant machinery to airliners has basically been a rush to identify and rectify faults, known as ‘corrective maintenance’. With the advent of preventive maintenance and methods such as reliability-centered maintenance, the focus has shifted to anticipating faults and planning maintenance tasks ahead of time, based on the reliability characteristics of the asset in question.

Condition-based maintenance, where assets are monitored in order to spot the early signs of a fault, before it occurs, has been around for a number of years. Here, non-destructive testing techniques, such as acoustic emission, thermography and vibration analysis, are used to detect the onset of faults that could lead to downtime and repairs. However, they provide little or no visibility on the remaining lifespan of the part.

As well as finding the root cause of a fault, maintenance personnel need to know the risk of keeping the part in service, so they can change it at the right time — not too early, which increases spare parts costs, nor too late, because an unexpected failure could be even more costly. They therefore need to measure this risk. In other words, determine the probability of a failure and how it increases with time.

To achieve the optimal balance in maintenance, parts need to be changed at just the right time. In other words, before a fault or failure occurs, thereby avoiding further damage and limiting the impact of system downtime and loss of production. Such ‘preventive’ repairs must also be scheduled to minimize disruption to production. Maintenance staff must therefore be able to calculate this optimum. Today, this is made possible with the arrival of predictive maintenance.

With all the instrumentation and sensors built into our factories and systems today, combined with the connectivity of the IoT, we now have access to a huge amount of potentially useful data for maintenance. However, it still needs to be extracted and interpreted. This is where data science comes in, providing a set of methods and tools for turning raw data into value-added, actionable information.

The data generated by the ERP system and machine control mechanisms in a factory, or the avionics suite on an airliner, for example, combined with maintenance reports, fault reports and external weather bulletins, are just some of the sources of information that can be usefully exploited. Data science helps us find correlations in these datasets, detect weak signals and develop predictive models.

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