Predicting the maintenance

It happens at the worst of times - late for a meeting, on the way to the rugby and even when you're desperate for the bathroom. When your car breaks down, you can moan in retrospect, acknowledging the signs that it needed urgent maintenance. Thanks to technology, more specifically the evolution and application of cognitive learning, these frustrating occurrences will become a thing of the past.

Connecting the things
Analyst house Gartner forecasts that there will be 20.8 billion connected ‘things’ worldwide by 2020. Enterprises that stick to an old ‘preventive’ data methodology are going to be left behind, as this approach accounts for a mere 20% of failures.

Predictive maintenance brings a proactive and resource saving opportunity. Predictive software can alert the manufacturer or user when equipment failure is imminent, but also carry out the maintenance process automatically ahead of time. This is calculated based on real time data, via metrics including pressure, noise, temperature, lubrication and corrosion to name a few.

Considering degradation patterns to illustrate the wear and tear of the vehicle in question, the production process is not subject to as high levels of interruption without the technology. By monitoring systems ‘as live’, breakdowns can be avoided prior to them happening.

It’s no longer a technological fantasy. Due to data in cars being collected for decades, researchers and manufacturers can gather insights that could be used to prepare predictive analytics. This will assist in predicting which individual cars will break down and need maintenance.

Now that the Internet of Things (IoT) is a reality, car manufacturers can use this information to offer timely and relevant additional customer services based on sophisticated software that can truly interrogate, interpret and use data. So who is going to be responsible for taking advantage of this technology?

Bolts and screws
Key management figures in the transport industry must commit to a maintenance management approach to implement a long-term technological solution. As described by R.Mobley, run-to-failure management sees an organisation refrain from spending money in advance, only reacting to machine or system failure. This reactive method may result in high overtime labour costs, high machine downtime and low productivity.

Similarly reactive, preventive maintenance monitors the mean-time-to-failure (MTTF), based on the principle that new equipment will be at its most vulnerable during the first few weeks of operation, as well as the longer it is used for. This can manifest itself in various guises, such as engine lubrication or major structural adjustments. However, predicting the time frame in which a machine will need to be reconditioned may be unnecessary and costly.

As an alternative option, predictive maintenance allows proactivity, ensuring lengthier time between scheduled repairs, whilst reducing the significant amount of crises that will have to be addressed due to mechanical faults. With a cognitive predictive model, meaning applications are able to teach themselves as they function, organisations will be able to foresee exactly why and when a machine will break down, allowing them to act to mitigate the effects to achieve zero downtime.

With that end goal in mind, predictive maintenance can be used at the start of the supply chain to predict which cars may be affected before they leave the factory, avoiding mass recalls in the process. Condition-based monitoring services in general can prove critical for a car manufacturer’s business model, doing wonders for a respective business’ brand equity.

For car lovers, diagnosing a fault in a device or appliance before it happens can be cost and time effective. Not only that, it will also provide peace of mind for drivers and passengers that their safety is being accounted for.

Starting now
Cognitive technology is multi-dimensional and will continue to provide opportunities across industries. With companies such as Google testing their Waymo self-driving cars, predictive maintenance software will be even more sought after in the automotive industry.

To keep up with the evolving landscape, it will be essential for manufacturers and leaders in the transport industry to deploy leading software from vendors that embrace cognitive predictive analytics and maintenance.