论文标题
可解释的机器学习模型，用于预测和解释车辆燃油消耗异常
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies
论文作者
论文摘要
识别舰队车辆燃料消耗的异常是优化消费和降低成本的关键方面。但是，仅此信息是不够的，因为车队运营商需要了解异常燃料消耗背后的原因。我们结合了无监督的异常检测技术，域知识和可解释的机器学习模型，以解释以功能相关性方面的潜在原因。这些解释用于生成有关燃料优化的建议，这些建议是根据两个不同的用户配置文件进行调整的：车队经理和车队运营商。通过来自与柴油机和汽油车相关的远程信息处理设备的实际数据评估结果。我们衡量有关模型性能的建议，并使用可解释的AI指标来比较代表性，忠诚，稳定性，对比度和与Apriori信念的一致性。可以实现的潜在燃料减少为35％。
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%.
