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Details

  • Name

    Narjes Davari
  • Role

    Assistant Researcher
  • Since

    01st November 2020
002
Publications

2022

A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set

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.