Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Compound fault diagnosis and sequential prognosis for electric scooter with uncertainties

Yu, M., Lu, H., Wang, H.ORCID: 0000-0003-2789-9530, Xiao, C. and Lan, D. (2020) Compound fault diagnosis and sequential prognosis for electric scooter with uncertainties. Actuators, 9 (4). Article 128.

PDF - Published Version
Download (9MB) | Preview
Free to read:
*No subscription required


This paper addresses diagnosis and prognosis problems for an electric scooter subjected to parameter uncertainties and compound faults (i.e., permanent fault and intermittent fault with non-monotonic degradation). First, the diagnostic bond graph in linear fractional transformation form is used to model the uncertain electric scooter and derive the analytical redundancy relations incorporating the nominal part and uncertain part, based on which the adaptive thresholds for robust fault detection and the fault signature matrix for fault isolation can be obtained. Second, an adaptive enhanced unscented Kalman filter is proposed to identify the fault magnitudes and distinguish the fault types where an auxiliary detector is introduced to capture the appearing and disappearing moments of intermittent fault. Third, a dynamic model with usage dependent degradation coefficient is developed to describe the degradation process of intermittent fault under various usage conditions. Due to the variation of degradation coefficient and the presence of non-monotonic degradation characteristic under some usage conditions, a sequential prognosis method is proposed where the reactivation of the prognoser is governed by the reactivation events. Finally, the proposed methods are validated by experiment results.

Item Type: Journal Article
Murdoch Affiliation(s): Engineering and Energy
Publisher: MDPI
Copyright: © 2020 by the authors
Item Control Page Item Control Page


Downloads per month over past year