Publications

This POSTnote by Ellie Smallwood summarises the challenges and options for enabling and encouraging of low-carbon actions by individuals in sectors with the highest emissions, which from the research undertaken as part of her 3 month UKRI Internship with POST. 

The automotive sector provides society with the means to move people and goods, however the increasing need for climate change mitigation places responsibility on automotive manufacturers to develop low-carbon technologies to meet these needs.

These rapid technological developments are prone to high costs, skill gaps, and sub-optimal energy-intensive practices, particularly within the testing environments for automotive components. Automated safety thresholds are in place to detect large fluctuations in real-time data, whilst the detection of other, more nuanced faults is manual, but these have the risk of undermining vehicle development. As such, this process is resource intensive, such as the personnel time and energy consumed to rerun tests containing faults, and the financial costs associated with these. Here we show the ability of unsupervised, data-driven machine learning based anomaly detection methods to identify anomalous time-periods within an automotive test, including faults in either the test component or in the test facility equipment, without the need for training data.

We compare the performance of three clustering algorithms –k-means, agglomerative clustering, and DBSCAN – based on their run time and ability to create defined anomalous clusters of the two anomalies. K-means was able to identify the two anomalies with eight total clusters in half the time of agglomerative clustering. DBSCAN clustered the data in half the time ofk-means however was unable to create defined anomalous clusters. 

These results illustrate the potential for unsupervised data-driven anomaly detection to operate within automotive manufacturer testing environments. These methods provide a low-cost digital solution to the resource demands associated with the traditional processes used by automotive manufacturers when developing sustainable transport options.

Positive attitudes towards digital twins: participants generally view digital twins positively and think that they could have a positive impact in the near-term.

High levels of trust: Participants reported high levels of trust in digital twins, even for applications that are not currently feasible. This suggests that there may be a tendency for over-trust that organisations should be aware of.

Beware of over-trust: organisations should be aware of potential over-trust in digital twins and ensure that users have realistic expectations of what a Digital Twin can and cannot do. Ways to do this include ‘grounding’ discussions of the Digital Twin in details about its capabilities and how it was developed and validated.