Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies
This work addresses the need for actionable insights in fleet management by providing interpretable explanations for fuel consumption anomalies, though it is incremental in applying existing techniques to a specific domain.
The paper tackled the problem of identifying and explaining anomalies in vehicle fuel consumption for fleet optimization, achieving a potential fuel reduction of around 35% through a method combining unsupervised anomaly detection and interpretable machine learning models.
Identifying anomalies in the fuel consumption of the vehicles of a fleet is a crucial aspect for optimizing consumption and reduce costs. However, this information alone is insufficient, since fleet operators need to know the causes behind anomalous fuel consumption. We combine unsupervised anomaly detection techniques, domain knowledge and interpretable Machine Learning models for explaining potential causes of abnormal fuel consumption in terms of feature relevance. The explanations are used for generating recommendations about fuel optimization, that are adjusted according to two different user profiles: fleet managers and fleet operators. Results are evaluated over real-world data from telematics devices connected to diesel and petrol vehicles from different types of industrial fleets. We measure the proposal regarding model performance, and using Explainable AI metrics that compare the explanations in terms of representativeness, fidelity, stability, contrastiveness and consistency with apriori beliefs. The potential fuel reductions that can be achieved is round 35%.