Detecting APS failures using LSTM-AE and anomaly transformer enhanced with human expert analysis

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Mumcuoğlu, Mehmet Emin and Farea, Shawqi Mohammed Othman and Ünel, Mustafa and Mise, Serdar and Unsal, Simge and Cevik, Enes and Yilmaz, Metin and Koprubasi, Kerem (2024) Detecting APS failures using LSTM-AE and anomaly transformer enhanced with human expert analysis. Engineering Failure Analysis, 165 . ISSN 1350-6307 (Print) 1873-1961 (Online)

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Abstract

This study develops a novel semi-supervised approach for detecting Air Pressure System (APS) failures in Heavy-Duty Vehicles (HDVs) by exploiting two modern Machine Learning (ML) models: Long Short-Term Memory Autoencoder (LSTM-AE) and Transformer for Anomaly Detection (TranAD), and enhancing their performance with human expertise. To tackle the failure detection problem, a dataset comprising 30 days of operational time-series data from 110 healthy vehicles with no recorded APS issues and 30 vehicles that experienced APS failures requiring road assistance was acquired. Several preprocessing steps are proposed and three key features are extracted as APS health indicators. These features are then utilized both in human expert analysis (HEA) and training of ML models. When compared to HEA, both LSTM-AE and TranAD models exhibit superior performance individually in APS failure detection, achieving F1 scores of 0.75 and 0.79 respectively, and the same accuracy of 91.4%. Further, the integration of HEA with those ML models enhances model effectiveness in all experimental results, especially in reducing false alarms that cause customer dissatisfaction. The TranAD model combined with human expert analysis achieved the best performance with an unprecedented 0.82 F1 score and 92.8% accuracy. In addition to presenting a new methodology for failure detection, this paper suggests a way for more efficient and reliable predictive maintenance practices for HDVs.
Item Type: Article
Uncontrolled Keywords: Air pressure system; Failure detection; Heavy-duty vehicles; LSTM autoencoder; Predictive maintenance; TranAD
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Mustafa Ünel
Date Deposited: 03 Sep 2024 11:52
Last Modified: 03 Sep 2024 11:52
URI: https://research.sabanciuniv.edu/id/eprint/49860

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