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

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Davari, N (Author) Other; Pashami, S (Author) Other; Veloso, B (Author) Other; Nowaczyk, S (Author) Other; Fan, Y (Author) Other; Pereira, PM (Author) Other; Rita Ribeiro (Author) FCUP; João Gama (Author) FEP

Conference proceedings International

Title: Advances in Intelligent Data Analysis XX - 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20-22, 2022, Proceedings

Pages: 39-52

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 7 Citations

Other information

Authenticus ID: P-00W-BT6

DOI: 10.1007/978-3-031-01333-1_4

Abstract (EN): This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13