Predictive Maintenance

In the context of the automotive industry, predictive maintenance is a maintenance strategy that monitors the vehicle and its components and attempts to predict potential faults or failures before they occur.

Necessary Data

The data necessary for predictions is collected from in-vehicle sensors and past maintenance records. But also, the data from a fleet of vehicles of the same make and model is used to look for common failure patterns. The sensors continuously monitor the vehicle and its components in real-time, leveraging IoT technology to translate the data. This information is then transmitted to the cloud for further analysis and prediction purposes.

The Process

Advanced analytics techniques, including machine learning algorithms, are employed to create data models and analyse and interpret them. The algorithms help in making predictions about potential failures in the vehicle by identifying patterns, trends, and outliers within the data. By using machine learning and artificial intelligence, it becomes possible to predict with a high level of accuracy when an asset may fail. The manufacturer or the driver then receives a notification about a possible future malfunction, allowing for proactive maintenance actions to be taken.

There lies the difference between predictive and preventive maintenance. For now, the latter is much more common and involves performing maintenance activities on a set schedule, regardless of the asset’s current condition. By doing so, one runs the risk of over-maintenance, which is not cost-effective and can result in a reduction in the component’s usable life. With predictive maintenance, vehicles are sent for maintenance only when it is absolutely required.

Digital Twinning

Predictive maintenance is largely possible thanks to the emergence of digital twins that use real-time data from a vehicle’s sensors. As a virtual counterpart of the actual asset, the digital twin captures real-time data and insights from the physical asset. In doing so, it accurately reflects the functionalities, working condition, and health of the physical counterpart.

By receiving data from various sensors and sources, it enables continuous monitoring, analysis, and prediction of potential maintenance needs or failures. Through machine learning and artificial intelligence techniques, the digital twin can help identify problems ahead of their manifestation on the physical vehicle.

Predictive maintenance offers significant benefits in terms of cost savings, improved efficiency, and enhanced vehicle reliability. With increasing numbers of connected vehicles and advancements in data analytics, the adoption of predictive maintenance techniques in the automotive industry is expected to grow steadily.