Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines

Hu, Yang; Palmé, Thomas; Fink, Olga

doi:10.1016/j.engappai.2016.10.010

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines
Authors:	Hu, Yang Palmé, Thomas Fink, Olga
DOI:	10.1016/j.engappai.2016.10.010
Published in:	Engineering Applications of Artificial Intelligence
Volume(Issue):	57
Page(s):	105
Pages to:	117
Issue Date:	2017
Publisher / Ed. Institution:	Elsevier
ISSN:	0952-1976
Language:	English
Subject (DDC):	006: Special computer methods
Abstract:	Early fault detection of engineering systems allows early warnings of anomalies and provides time to initiate proactive mitigation actions before the anomaly has developed to a problem that either requires extensive maintenance or affects the productivity of the system. In this paper, a new fault detection method using signal reconstruction based on Auto-Associative Extreme Learning Machines (AAELM) is proposed. AAELM are applied for fault detection on an artificially generated dataset to test the performance of the algorithm under controlled conditions and a real case study based on condition monitoring data from a combined-cycle power plant compressor. The performance of AAELM is compared to that of two other commonly used signal reconstruction methods: Auto-Associative Kernel Regression (AAKR) and Principal Component Analysis (PCA). The results from the two case studies demonstrate that AAELM achieve a smaller reconstruction error, shorter detection delay, lower spillover and a higher distinguishability compared to AAKR and PCA on the evaluated datasets. The obtained results are generalized to elaborate guidelines for industrial users for selecting suitable signal reconstruction algorithms based on their specific requirements and boundary conditions.
URI:	https://digitalcollection.zhaw.ch/handle/11475/13949
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Data Analysis and Process Design (IDP)
Appears in collections:	Publikationen School of Engineering

Files in This Item:

There are no files associated with this item.

Show full item record

Hu, Y., Palmé, T., & Fink, O. (2017). Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines. Engineering Applications of Artificial Intelligence, 57, 105–117. https://doi.org/10.1016/j.engappai.2016.10.010

Hu, Y., Palmé, T. and Fink, O. (2017) ‘Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines’, Engineering Applications of Artificial Intelligence, 57, pp. 105–117. Available at: https://doi.org/10.1016/j.engappai.2016.10.010.

Y. Hu, T. Palmé, and O. Fink, “Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines,” Engineering Applications of Artificial Intelligence, vol. 57, pp. 105–117, 2017, doi: 10.1016/j.engappai.2016.10.010.

HU, Yang, Thomas PALMÉ und Olga FINK, 2017. Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines. Engineering Applications of Artificial Intelligence. 2017. Bd. 57, S. 105–117. DOI 10.1016/j.engappai.2016.10.010

Hu, Yang, Thomas Palmé, and Olga Fink. 2017. “Fault Detection Based on Signal Reconstruction with Auto-Associative Extreme Learning Machines.” Engineering Applications of Artificial Intelligence 57: 105–17. https://doi.org/10.1016/j.engappai.2016.10.010.

Hu, Yang, et al. “Fault Detection Based on Signal Reconstruction with Auto-Associative Extreme Learning Machines.” Engineering Applications of Artificial Intelligence, vol. 57, 2017, pp. 105–17, https://doi.org/10.1016/j.engappai.2016.10.010.