Cognitive Predictive maintenance (CPdM) In Automotive is aiming to clearly identify vehicle maintenance issues before they occur. In line with the trend toward autonomous vehicle, by extracting data from repairs with vehicle sensor data, predictive data analytics can find some useful correlations that would be hard for humans to identify. Most 21st century “smart” cars are already equipped with embedded computer systems that can deliver data to technicians for diagnostic purposes through a two-way communication process where technicians connect their service computer to a vehicle’s onboard computer to deliver form diagnostic codes to the service centre.
Automated onboard notifications that signal maintenance needs are also already present in most cars manufactured in the last two decades. We see this anytime an oil light or a service notification appears on the dashboard console. Some cars even have sensors that signal when tire pressure is low, reminding the user to act accordingly. More functionality will be invent as Internet of Things (IoT) and automation being matured. While these methods can spur needed maintenance resolution, service contract must be first initiated by the vehicle user, which is something that IoT solutions can change. If, for example, vehicle system data was shared with a consumer’s service centre, it could allow for timely service or maintenance reminders that are customized for the user’s vehicle profile.
Also, with AI , Deep Learning (DL) and predictive maintenance can show their function. Service engineers can use cognitive analysis of multiple sources of data collected from sensors and service records to identify and solve problems in vehicles much before the “check engine” light goes on, eliminating safety risks and costly repairs, and delivering superior customer experience. Also, it helps the engineers plan inventory of parts required for maintenance much better since they know of the problems ahead of time.
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