Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics

Peter Wolf, Artur Mrowca, Tam Thanh Nguyen, Bernard Baker, Stephan Gunnemann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

Fault detection in vehicles is currently carried out using model-based or rule-based approaches. Due to advances in automotive technology such as autonomous driving and further connectivity, the complexity of vehicles and their subsystems increases continuously. As a consequence, models and rule-based systems for fault detection become more complex and require more extensive implementation effort and expert knowledge not only within but also across several domains. Besides, vehicles produce rich amounts of data including valuable information for fault detection. These amounts cannot fully be considered by current fault detection approaches. Deep neural networks offer promising capabilities to address these challenges by allowing automated model generation for fault detection without extensive domain knowledge using vast amounts of in-vehicle data. Hence, in this work a data-driven automotive diagnostics approach to fault detection with deep neural networks is proposed in two steps. First, a novel data-driven diagnostics process to learn data-driven algorithms on in-vehicle data is presented. Second, as a key element of this process, a novel fault detection model is proposed using a combination of convolutional and long short-term memory neural networks. To demonstrate the suitability for fault detection, the model is evaluated on internal engine control unit signals for pre-ignition detection in high-pressure turbocharged petrol engines. A classical machine learning processing pipeline and each neural network type separately are used as baselines for a performance comparison. Results show that the proposed model is a promising approach to data-driven automotive diagnostics outperforming all implemented baselines.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages176-183
Number of pages8
ISBN (Electronic)9781728103235
DOIs
StatePublished - 7 Dec 2018
Event21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States
Duration: 4 Nov 20187 Nov 2018

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2018-November

Conference

Conference21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Country/TerritoryUnited States
CityMaui
Period4/11/187/11/18

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