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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