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Baseline Selection for Integrated Gradients in Predictive Maintenance of Volvo Trucks’ Turbocharger
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0003-2590-6661
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-0051-0954
2023 (English)In: VEHICULAR 2023: The Twelfth International Conference on Advances in Vehicular Systems, Technologies and Applications / [ed] Reiner Kriesten; Panos Nasiopoulos, International Academy, Research and Industry Association (IARIA), 2023, p. 29-36Conference paper, Published paper (Refereed)
Abstract [en]

The new advances in Vehicular Systems and Technologies have resulted in a sheer increase in the number of connected vehicles. These connected vehicles use IoT technologies to communicate operational signals with the OEMs, such as the vehicle’s speed, torque, temperature, load, RPM, etc. These signals have provided an unprecedented opportunity to adaptively monitor the status of each piece of the vehicle’s equipment and discover any possible risk of failure before it happens. This emerging field of study is called predictive maintenance (also known as condition-based maintenance) and has recently received much attention. In this paper, we apply Integrated Gradients (IG), an XAI method until now primarily used on image data, on datasets containing tabular and time-series data in the domain of predictive maintenance of trucks’ turbochargers. We evaluate how the results of IG differ, in these new settings, for various types of models. In particular, we investigate how the change of baseline can affect the outcome. Experimental results verify that IG can be applied successfully to both sequenced and non-sequenced data. Contrary to the opinion common in the literature, the gradient baseline does not affect the results of IG significantly, especially on models such as RNN, LSTM, and GRU, where the data contains time series; the effect is more visible for models like MLP with non-sequenced data. To confirm these findings, and to understand them deeper, we have also applied IG to SVM models, which gave the results that the choice of gradient baseline has a significant impact on the performance of SVM. (c) IARIA, 2023

Place, publisher, year, edition, pages
International Academy, Research and Industry Association (IARIA), 2023. p. 29-36
Keywords [en]
Explainable AI (XAI), Predictive Maintenance, Integrated Gradients, Machine Learning
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-50093ISBN: 978-1-68558-061-2 (print)OAI: oai:DiVA.org:hh-50093DiVA, id: diva2:1742654
Conference
VEHICULAR 2023, The Twelfth International Conference on Advances in Vehicular Systems, Technologies and Applications, Barcelona, Spain, March 13-17, 2023
Funder
Knowledge FoundationAvailable from: 2023-03-10 Created: 2023-03-10 Last updated: 2024-02-14Bibliographically approved

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Rahat, MahmoudSheikholharam Mashhadi, Peyman

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