A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels

TL;DR

This paper presents a robust anomaly detection system for vehicles using sensor data from functional groups, achieving high accuracy and true anomaly prediction rates, and introduces the VePRO dataset for this purpose.

Contribution

The paper introduces a novel multi-phased anomaly detection approach using Temporal Convolution Networks and the new VePRO dataset for vehicle sensor data analysis.

Findings

96% anomaly detection accuracy

91% true anomaly prediction rate

Improved performance with multiple FWG sensor channels

Abstract

A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs). Examples of these FWGs include the engine system, transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels that gauge vehicular operating conditions. This data rich environment is conducive to the development of Predictive Maintenance (PdM) technologies. Undercutting various PdM technologies is the need for robust anomaly detection models that can identify events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal vehicular operational behavior. In this paper, we introduce the Vehicle Performance, Reliability, and Operations (VePRO) dataset and use it to create a multi-phased approach to anomaly detection. Utilizing Temporal Convolution Networks (TCN), our anomaly detection system can achieve 96% detection accuracy and accurately predicts 91% of true anomalies. The performance of our anomaly detection system improves when sensor channels from multiple FWGs are utilized.