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

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...

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Bibliographic Details
Published in:Engineering failure analysis Vol. 165; p. 108811
Main Authors: Mumcuoglu, Mehmet E., Farea, Shawqi M., Unel, Mustafa, Mise, Serdar, Unsal, Simge, Cevik, Enes, Yilmaz, Metin, Koprubasi, Kerem
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2024
Subjects:
Air pressure system
Failure detection
Heavy-duty vehicles
LSTM autoencoder
Predictive maintenance
TranAD
Failure detection
Heavy-duty vehicles
TranAD
LSTM autoencoder
Air pressure system
Predictive maintenance
ISSN:1350-6307
Online Access:Get full text
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Summary: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. •An effective semi-supervised framework for time-series-based predictive maintenance was proposed.•Air Pressure System failures were detected using LSTM Autoencoder and Anomaly Transformer.•Manual analysis based on patterns in domain knowledge-based indicative features was conducted.•The integration of manual analysis with the models led to a significant reduction in false alarms.
ISSN:1350-6307
DOI:10.1016/j.engfailanal.2024.108811