Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data

• URL: http://arxiv.org/abs/2202.07592v3
• Date: Mon, 9 Sep 2024 16:18:54 GMT
• Title: Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data
• Authors: Anthony Geglio, Eisa Hedayati, Mark Tascillo, Dyche Anderson, Jonathan Barker, Timothy C. Havens,
• Abstract summary: 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.
• Score: 1.7933377464816112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work investigates a practical and novel method for automated unsupervised fault detection in vehicles using a fully convolutional autoencoder. The 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. Data was collected by engineers at Ford Motor Company from numerous sensors over several drive cycle variations. This study provides evidence of the anomaly detecting capability of our trained autoencoder and investigates the suitability of our autoencoder relative to other unsupervised methods for automatic fault detection in this data set. Preliminary results of testing the autoencoder on the powertrain sensor data indicate the data reconstruction approach availed by the autoencoder is a robust technique for identifying the abnormal sequences in the multivariate series. These results support that irregularities in hybrid-electric vehicles' powertrains are conveyed via sensor signals in the embedded electronic communication system, and therefore can be identified mechanistically with a trained algorithm. Additional unsupervised methods are tested and show the autoencoder performs better at fault detection than outlier detectors and other novel deep learning techniques.

Related papers

• Automated Automotive Radar Calibration With Intelligent Vehicles [73.15674960230625]
  We present an approach for automated and geo-referenced calibration of automotive radar sensors. Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles. Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner.
  arXiv  Detail & Related papers  (2023-06-23T07:01:10Z)
• 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)
• A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels [0.0]
  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.
  arXiv  Detail & Related papers  (2022-09-14T14:33:07Z)
• Transfer Learning for Fault Diagnosis of Transmission Lines [55.971052290285485]
  A novel transfer learning framework based on a pre-trained LeNet-5 convolutional neural network is proposed. It is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source neural network to predict a dissimilar target dataset.
  arXiv  Detail & Related papers  (2022-01-20T06:36:35Z)
• Anomaly Detection in Multi-Agent Trajectories for Automated Driving [2.5211566369910967]
  Similar to humans, automated vehicles are supposed to perform anomaly detection. Our innovation is the ability to jointly learn multiple trajectories of a dynamic number of agents.
  arXiv  Detail & Related papers  (2021-10-15T08:07:31Z)
• 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)
• Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders [0.0]
  We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, without targeting a specific kind of source.
  arXiv  Detail & Related papers  (2021-07-27T09:56:49Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• Anomaly Detection with SDAE [2.9447568514391067]
  A Simple, Deep, and Supervised Deep Autoencoder were trained and compared for anomaly detection over the ASHRAE building energy dataset. The Deep Autoencoder perfoms the best, however the Supervised Deep Autoencoder outperforms the other models in total anomalies detected.
  arXiv  Detail & Related papers  (2020-04-09T07:22:08Z)

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.