Abstract
Rotational machines such as direct current (dc) motors might be exposed to unexpected failures, which can cause production delays or safety problems. These failures must be detected immediately before the machines’ condition worsens or fails. A fault detection strategy can be used to detect the faults in the machines. This study aims to improve the adaptive observer-based fault detection techniques by implementing the low pass and Kalman filters. The dc motor was modelled in a state-space system in the simulation, and the encoder was modelled to have faults. From results, Kalman filter can tolerate the encoder fault’s effect and better estimates the actual states than the low pass adaptive observer.
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Acknowledgements
The Ministry of Higher Education Malaysia supports the work with FRGS Project Code: FRGS/1/2019/TK04/USM/02/12.
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Alias, N.D., Mokhtar, R.M. (2024). Enhancement of Adaptive Observer for Fault Detection in Direct Current Motor System Using Kalman Filter. In: Ahmad, N.S., Mohamad-Saleh, J., Teh, J. (eds) Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. RoViSP 2021. Lecture Notes in Electrical Engineering, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-99-9005-4_34
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DOI: https://doi.org/10.1007/978-981-99-9005-4_34
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