eprintid: 10164503
rev_number: 10
eprint_status: archive
userid: 699
dir: disk0/10/16/45/03
datestamp: 2023-02-08 16:51:32
lastmod: 2024-02-05 07:10:05
status_changed: 2023-02-08 16:51:32
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Guo, Muzhuang
creators_name: Guo, Chen
creators_name: Zhang, Chuang
creators_name: Zhang, Xinyu
creators_name: Liu, Yuanchang
title: Intelligent fault detection algorithm based on Hi/H∞ optimization and a cascaded neural networks
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F45
keywords: Integrated navigation system, Robust fault detection, Hi/H∞ optimization, Cascaded neural network
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
abstract: Autonomous surface ships (ASSs) have attracted attention owing to their ability to perform various tasks in complex and challenging aquatic environments without relying on a crew. However, they require reliable sensors to ensure navigational safety. In this study, a robust and intelligent fault detection algorithm was designed for the integrated navigation system of an ASS. First, a residual observer-based fault detection algorithm using Hi/H∞ optimization is proposed to deal with process disturbances and measurement noise. Such noise can be modeled under the condition of a bounded l2-norm to account for the sensitivity and robustness of the residual observer against random noise with unknown properties. However, this fault detection algorithm is insensitive to soft faults, which manifest as noise characterized by a small amplitude and slow variation. Conventional strategies for evaluating the fault detection threshold rely on human experience, which is insufficiently sophisticated for fault detection. Therefore, a cascaded neural network is proposed for optimizing the fault detection algorithm when the amount of training data is limited. The cascaded neural network consists of a multi-feature time domain network, a frequency-domain fault detection network as well as a decision-level fusion network. The proposed algorithm was verified in simulations as well as on historical data collected from real ship sensors. The results demonstrated that the proposed algorithm offers intelligent fault detection, including soft faults, with a low false alarm rate for integrated navigation systems.
date: 2023-03-15
date_type: published
publisher: Elsevier
official_url: https://doi.org/10.1016/j.oceaneng.2023.113835
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2004500
doi: 10.1016/j.oceaneng.2023.113835
lyricists_name: Liu, Yuanchang
lyricists_id: YLIUA09
actors_name: Liu, Yuanchang
actors_id: YLIUA09
actors_role: owner
full_text_status: public
publication: Ocean Engineering
volume: 272
article_number: 113835
citation:        Guo, Muzhuang;    Guo, Chen;    Zhang, Chuang;    Zhang, Xinyu;    Liu, Yuanchang;      (2023)    Intelligent fault detection algorithm based on Hi/H∞ optimization and a cascaded neural networks.                   Ocean Engineering , 272     , Article 113835.  10.1016/j.oceaneng.2023.113835 <https://doi.org/10.1016/j.oceaneng.2023.113835>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10164503/2/Liu_Intelligent%20fault%20detection%20algorithm_AAM.pdf