Today’s automotive market is embracing a connected society. The rise of artificial intelligence (AI) in the market is providing drivers with the ability to understand the issues of others and prevent any occurrence before it impacts their vehicles.
AI allows for systems to take data, and understand it while also producing algorithms and codes to detect patterns and behaviours. It is also able to learn, and by doing this, it can develop an understanding of information from vehicles, and their maintenance, to analyse potential faults before they occur. This information is then fed to customers under the guise of predictive maintenance.
Predictive maintenance utilises data from vehicles on faults, driving patterns and more, and is able to feed this information to other users. This is especially true in the fleet market, with management software now integrating vehicle data into servers. Additionally, regular drivers may also be informed of potential pitfalls, depending on their service level.
There are a number of benefits for predictive maintenance, from reducing unexpected breakdowns, extending vehicle life and lowering maintenance costs.
While predictive maintenance offers a benefit to drivers and businesses, how can garages ensure they remain on top of regular work, as well as repairing faults that have yet to occur?
While AI is largely involved in analysing trends, having vast amounts of data from both vehicle manufacturers and repairers can help businesses to diagnose faults and potential issues quickly. This can help to reduce repair time, meaning a vehicle spends less time in the workshop, and customers spend less on labour costs.
This provides a different form of predictive maintenance. Rather than knowing a fault before it occurs, it provides information on what a fault may be. This gives a jumping-off point for technicians to repair a vehicle more quickly.
When a vehicle is plugged into a diagnostic tool, the standard pathway is to find what is causing a fault from the trouble code provided. We provide any information relating to that code, enabling technicians to trace the problem as quickly as possible.
But the data goes further. Not only does it allow for fault tracing, but our information also incorporates known fixes for particular components and issues, and shows them in the area required. There are also links to component testing included, providing quicker access to test procedures and data.
This aids the time taken to diagnose faults and test components via the engine management system.
For example, a diagnostic tool may report a fault with a camshaft position sensor. Finding the source of the issue can take some time. But with Autodata, any information relating to potential causes and known fixes can be offered. Details on the sensor, reported problems, and testing are provided from the data gathered and analysed by the company.
This may, or may not, solve the problem. But it offers technicians a starting point to investigate, and could see a lot of time shaved off some of the more difficult jobs.
So, while the rise of AI may lead to vehicles being fixed before required, understanding data and providing that information to technicians is just as important to ensure vehicle repair and maintenance are carried out efficiently.
