A Temporal Anomaly Detection System for Vehicles utilizing Functional
Working Groups and Sensor Channels
- URL: http://arxiv.org/abs/2209.06828v1
- Date: Wed, 14 Sep 2022 14:33:07 GMT
- Title: A Temporal Anomaly Detection System for Vehicles utilizing Functional
Working Groups and Sensor Channels
- Authors: Subash Neupane, Ivan A. Fernandez, Wilson Patterson, Sudip Mittal,
Shahram Rahimi
- Abstract summary: We introduce the Vehicle Performance, Reliability, and Operations dataset and use it to create a multi-phased approach to anomaly detection.
Our anomaly detection system can achieve 96% detection accuracy and accurately predicts 91% of true anomalies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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