Connectivity and the Internet of Things are changing the landscape of the automotive industry, and as these technologies are gradually implemented, there are a number of interesting areas of innovation.
Remote diagnostics is one such area where technology is paving the way towards revolutionary new concepts of vehicle maintenance, including the use of artificial intelligence and deep learning neural networks to develop advanced prognostic systems.
Research by industry analysts Technavio in 2017 forecasts that the global automotive remote diagnostics market will grow at a CAGR of 26.79% during the period from 2017 to 2024. This is indicative of the progression towards prognostics - or predictive diagnostics - and away from traditional diagnostics.
Vdiagtool started to change from traditional diagnosis to predictive diagnosis in 2017. In recent years, many remote diagnosis products have been launched.
The Drivers Behind Remote & Automotive Diagnostics
New vehicles, complete with 100 or more ECUs, have become highly complex networks and this increases the pressure to develop more efficient tools and systems to effectively diagnose the ECUs. The current trends in legislation and functionality offer opportunity but also come with risk as complexity increases. However, services such as ADAS and eCall are already taking advantage of connectivity and the push towards autonomous driving will see further development of car-to-X communication. The huge increase in the amount of data gathered from the vehicle and the ability to process it offers a variety of benefits in terms of maintenance.
One of these possibilities is vehicle diagnostics over the entire lifecycle, from engineering to manufacturing to after-sales. Technical experts could, for example, be based in a regional technical centre from which they could access data from an individual vehicle’s ECUs in order to diagnose faults. This information could be relayed to the service centre if local engineers are unable to diagnose locally.
This, however, is just the tip of the iceberg in terms of innovation. The continuous monitoring of real-time data via a wireless network opens up a wide range of possibilities. Faults and problems could be determined in real-time and potential faults could be highlighted before they result in greater problems or damage. Drivers could be alerted to issues and directed to the nearest service centre, while information and diagnostics could be sent in advance to the local service centre so that they are prepared for the repair.
The cyclical data collected from a large number of vehicles also enables the monitoring of ageing processes across a large sample and can instruct preventative maintenance. Some manufacturers are already utilizing over-the-air software updates, and with more sophisticated data analysis and diagnostics, this is an area which offers huge potential savings in terms of recalls and warranty repairs. The introduction of remote diagnostics services also reduces the gap in the value chain and allows manufacturers to communicate directly with customers to offer better services.
Other driving forces behind remote diagnostic development include fleet management and MaaS (Mobility as a Service). For fleet managers, faster analysis of faults and issues leads to quicker action. This reduces downtime and means that vehicles can be repaired before problems escalate. Mobility as a service is gaining traction in large cities around the world as more consumers are moving away from ownership to shared services. Remote diagnostics will be crucial to passenger vehicle fleets which aim to provide a comprehensive and reliable service.
