Wednesday 19th February 2020

Do you recall a time when we used to recall vehicles?

Published on June 21st, 2018

Ruban Phukan,VP Product, Cognitive-First, Progress, explains why fixing what’s broken is not the best approach to fixing known faults in vehicles. Predictive maintenance is a far smarter option. It combines IoT, artificial intelligence, machine learning and predictive analytics to reduce costs, increase convenience and improve safety.

Nothing good tends to come from something that is broken.It’s bad enough to have to deal with problems in the factory. The average manufacturer deals with around 800 hours of downtime annually, which translates to almost a million pounds in lost revenue.

Dealing with problems out on the road is even more costly is for automotive makers: back in 2014, over 64 million vehicles were taken off the market due to defects. Since the start of 2018 we have seen Tesla recall 123,000 Model S cars; Ford recall nearly 350,000 F-series pickup trucks and Expedition SUVs; and Toyota recall 21,000 Toyota and Lexus cars.

Stepping into the unknown

After the Tesla announcement, its shares fell 4%, a decline made all the more pertinent in June when almost 10% of its global workforce was made redundant as the manufacturer looked to steady the ship and win back investors’ confidence in the business model. Ultimately recalls, mistakes and broken parts make headlines around the world, costing brands millions in repairs and untold reputational damage.

For years, the conventional approach was to fix what was broken. Then more manufacturers began to take a preventative approach.It used a combination of physics-based models of the machines as well as knowledge of past failures to determine at what interval a particular type of machine is likely to need repairing to catch known problems in time. This approach was effective for handling known problems but couldn’t detect the unknown problems which happen much more often in the field.

Now, with advances in cognitive learning and the proliferation of IoT sensors across production lines, we are entering a new stage of predictive maintenance. This is where AI, machine learning and predictive analytics can enable manufacturers to gain full visibility and control of manufacturing processes to not only prepare for the known issues, but also effectively predict and prevent unknown problems.

Predicting from the factory to the road

We can’t turn back time, yet, despite all our progress with technology What we can do is almost as impressive: new cognitive technologies can analyse data in ways that were never thought possible. Previous manual data modelling allowed manufacturers to use their failures to predict future ones. AI-powered platforms use cognitive learning, which not only uses failures to teach themselves, but can learn to anticipate ones that have never happened before.

The importance of this cannot be understated, as many of the issues that cause recalls tend to be new ones. Through teaching themselves with data from sensors, cognitive applications can learn the conventional operating conditions and environmental influences on machines at a micro level, going beyond the macro-patterns that the human brain tends to spot. This means that micro-anomalies and small changes that can go undetected in the quality check process can now be identified automatically as they occur. In this way, breakdowns or faults can be predicted ahead of time, before they spark recalls or cause downtime.

To make sure the technology is being used most effectively, manufacturers should look to deploy it across a number of touchpoints in the production process. During the initial manufacturing process, cognitive predictive maintenance can identify and share alerts on in-line defects. This allows for problems to be addressed long before the product goes to market.

Looking after the backstage actors as well

It’s not just the vehicles for the consumer market that can be monitored; cognitive predictive maintenance also allows for the production line machinery being used to build the parts to keep running smoothly as well. Using industrial IoT and sensors found throughout manufacturing plants these days, we can now understand the health of a machine in minute detail.

Ruban Phukan

This is achieved through the creation of ‘Digital Twins’, which are highly detailed digital copies of physical machines. They allow a manufacturer to run an exact digital simulation of all of its physical machines, in parallel at all times, giving machine the tools to foresee their own future from a maintenance perspective. The introduction of cognitive learning into this process means that the digital twin can analyse and report back on its own health, helping to fix issues before they cause problems.

But what about when the product goes to market?

The role of predictive maintenance doesn’t stop once the vehicle is out of the factory. In the field, the cognitive predictive maintenance model can be derived from multiple data sources, like data collected from connected vehicles and service records, test data on parts that have been replaced and even external factors like weather conditions, road conditions, attention and biometric sensors for drivers, and social media. This data can be used to identify and solve problems faster, to either avoid a breakdown altogether and initiate the maintenance procedure sooner and plan parts inventory and field personnel availability better.

Looking ahead

The use of cognitive predictive maintenance is not a luxury or a toy reserved for the biggest manufacturers; this technology is becoming essential in an industry where the smallest advantage can mean millions. The most exciting part is that this is just the start in terms of where cognitive technology will take the automotive industry.

The author of this blog is Ruban Phukan,VP Product, Cognitive-First, Progress

Comment on this article below or via Twitter: @IoTNow OR @jcIoTnow