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A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, S-30118 Halmstad, Sweden..ORCID-id: 0000-0002-3797-4605
Qom Univ Technol, Fac Elect & Comp Engn, Qom 151937195, Iran..
Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, S-30118 Halmstad, Sweden..
Univ New Brunswick UNB, Canadian Inst Cybersecur CIC, Fredericton, NB E3B 9W4, Canada..
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2023 (Engelska)Ingår i: Sensors, E-ISSN 1424-8220, Vol. 23, nr 12, artikel-id 5621Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system's effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

Ort, förlag, år, upplaga, sidor
MDPI, 2023. Vol. 23, nr 12, artikel-id 5621
Nyckelord [en]
breakdown prediction, optimization, deep neural networks, ensemble learning
Nationell ämneskategori
Farkostteknik
Identifikatorer
URN: urn:nbn:se:mau:diva-61954DOI: 10.3390/s23125621ISI: 001015804000001PubMedID: 37420787Scopus ID: 2-s2.0-85163933766OAI: oai:DiVA.org:mau-61954DiVA, id: diva2:1788878
Tillgänglig från: 2023-08-17 Skapad: 2023-08-17 Senast uppdaterad: 2023-08-22Bibliografiskt granskad

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Khoshkangini, Reza

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