Details
Name
Narjes DavariCluster
Computer ScienceRole
Assistant ResearcherSince
01st November 2020
Nationality
IrãoCentre
Artificial Intelligence and Decision SupportContacts
+351220402963
narjes.davari@inesctec.pt
2022
Authors
Davari, N; Pashami, S; Veloso, B; Fan, YT; Pereira, PM; Ribeiro, RP; Gama, J; Nowaczyk, S;
Publication
ADVANCES IN INTELLIGENT DATA ANALYSIS XX, IDA 2022
Abstract
This study applies a data-driven anomaly detection frame-work based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed frame-work efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network.
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