Abstract
Accurate prediction of faults before they occur is vital because the intricate, uncertain, and intercorrelated natures of industrial processes can lead to multiple component failures or to a complete shutdown of the overall prediction cycle. While the first principle-based fault detection approach demands significant expert knowledge and is component-specific, learning-based approaches offer a plausible alternative because of their learning capability of offline data. Learning-based fault detection and diagnosis still deserve in-depth investigation because current approaches must happen offline, are static, and must be supervised; this makes them hardly applicable for the live scenarios of industrial processes. This chapter proposes a novel approach using an evolving type-2 random vector functional link network, which combines the meta-cognitive learning concept with the random vector functional link theory. The efficacy of evolving type-2 random vector functional link networks was validated with an experimental study on diagnosing different fault conditions of induction motors – namely broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems – using a laboratory-scale test rig. Our algorithm was compared with other prominent algorithms and was found to deliver state-of-the-art performance in terms of accuracy, simplicity, and scalability.
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Za’in, C., Pratama, M., Prasad, M., Puthal, D., Lim, C.P., Seera, M. (2018). Motor Fault Detection and Diagnosis Based on a Meta-cognitive Random Vector Functional Link Network. In: Sayed-Mouchaweh, M. (eds) Fault Diagnosis of Hybrid Dynamic and Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-74014-0_2
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DOI: https://doi.org/10.1007/978-3-319-74014-0_2
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