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.

Related papers

• A Survey of Anomaly Detection in In-Vehicle Networks [2.440962475284596]
  Problems that may occur in the vehicle's physical parts or malicious attacks may cause anomalies in the CAN traffic, impairing the correct vehicle operation. This paper reviews the research on anomaly detection for in-vehicle networks. Our main focus is the evaluation of methods used for CAN bus anomaly detection together with the datasets used in such analysis.
  arXiv  Detail & Related papers  (2024-09-11T11:45:18Z)
• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning [9.100418852199082]
  We propose a novel framework based on unsupervised machine learning for early anomaly detection in AMS circuits. The proposed approach involves injecting anomalies at various circuit locations and individual components to create a diverse and comprehensive anomaly dataset. By monitoring the system behavior under these anomalous conditions, we capture the propagation of anomalies and their effects at different abstraction levels.
  arXiv  Detail & Related papers  (2024-04-02T04:33:03Z)
• Electrical Grid Anomaly Detection via Tensor Decomposition [41.94295877935867]
  Previous work has shown that dimensionality reduction-based approaches can be used for accurate identification of anomalies in SCADA systems. In this work, we novelly apply the tensor decomposition method Canonical Polyadic Alternating Poisson Regression with a probabilistic framework, to identify anomalies in SCADA systems. In our experiments, we model real-world SCADA system data collected from the electrical grid operated by Los Alamos National Laboratory.
  arXiv  Detail & Related papers  (2023-10-12T18:23:06Z)
• DARTH: Holistic Test-time Adaptation for Multiple Object Tracking [87.72019733473562]
  Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving. Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed. We introduce DARTH, a holistic test-time adaptation framework for MOT.
  arXiv  Detail & Related papers  (2023-10-03T10:10:42Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder [1.911678487931003]
  This paper focuses on anomaly detection for time series data in large-scale fluid handling plants with dynamic components. We introduce a Two-Stage AutoEncoder (TSAE) as an anomaly detection method suitable for such plants.
  arXiv  Detail & Related papers  (2022-05-20T01:41:39Z)
• Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data [1.7933377464816112]
  This work investigates a practical and novel method for automated unsupervised fault detection in vehicles using a fully convolutional autoencoder. Results demonstrate the algorithm we developed can detect anomalies which correspond to powertrain faults by learning patterns in the multivariate time-series data of hybrid-electric vehicle powertrain sensors. Additional unsupervised methods are tested and show the autoencoder performs better at fault detection than outlier detectors and other novel deep learning techniques.
  arXiv  Detail & Related papers  (2022-02-15T17:28:42Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Bayesian Autoencoders for Drift Detection in Industrial Environments [69.93875748095574]
  Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift)
  arXiv  Detail & Related papers  (2021-07-28T10:19:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.