Robust actuator fault detection for an induction motor via genetic-algorithm optimisation

Odofin, Sarah, Gao, Zhiwei, Liu, Xiaoxu and Sun, Kai (2016) Robust actuator fault detection for an induction motor via genetic-algorithm optimisation. In: ICIEA 2016 - 11th IEEE Conference on Industrial Electronics and Applications, 5th - 7th June 2016, Hefei, China.

Full text not available from this repository.
Official URL:


In this study, an application of genetic algorithm optimization-based fault detection for an induction motor with actuator faults is addressed. The frequencies of the modelling errors are identified from the Fourier transform of the measurement outputs, and a multi-objective cost function is constructed for minimizing the effects from the dominant modelling errors components while maximizing the influences from the actuator faults to the residual. Abrupt faults and incipient faults are both investigated, also the effectiveness of the proposed fault detection methods are demonstrated by using experimental/simulation studies of the induction motor.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Fault detection; Genetic Algorithms; Optimization; Fourier Transform; Induction Moto; Hybrid Fault Detection
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Dr Zhiwei Gao
Date Deposited: 06 Dec 2016 10:19
Last Modified: 12 Oct 2019 12:13

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics