Vehicle operating state anomaly detection and results virtual reality interpretation

Abstract

Nowadays, with the increasing complexity of vehicle operating environment, vehicle operating reliability becomes growing essential and has attracted significant attention in recent decades. The majority of vehicle operating activities are organized in the format of time series data. Many conventional approaches are proposed to deal with time-series data. Autoregressive Integrated Moving Average (ARIMA) approach is one of the most widely used methods to analyze time-series data. In this paper, an ARIMA-based anomaly detection framework is developed to identify abnormal states of the vehicles based on the multiple-channel operating time series data. The state anomaly is captured by the deviation of real-time values at different channels from the predictions. After abnormal operating states are identified, a novel immersive Virtual Reality (VR) tool is applied to visualize the difference between normal and abnormal status. The combination of ARIMA anomaly detection and VR visualization for vehicle multiple-channel operating time-series data would provide a robust way to present the results of vehicle operating status. A large scale data set of 14 days operational performance channels of a specific vehicle is utilized to validate the performance of the proposed approach.