Majid Hussain^1,2,*, Tayab Din Memon^3,4, Imtiaz Hussain⁵, Zubair Ahmed Memon³, Dileep Kumar²

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 435-470, 2022, DOI:10.32604/cmes.2022.020583

Abstract Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown. Recently, Motor Current Signature Analysis (MCSA) is widely reported as a condition monitoring technique in the detection and identification of individual and multiple Induction Motor (IM) faults. However, checking the fault detection and classification with deep learning models and its comparison among themselves or conventional approaches is rarely reported in the literature. Therefore, in this work, we present the detection and identification of induction motor faults with MCSA and three Deep Learning (DL) models namely MLP, LSTM, and… More >

Mokhtar Aly^1,2, Hegazy Rezk^3,4,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2283-2299, 2021, DOI:10.32604/cmc.2021.014786

Abstract Fuzzy logic control (FLC) systems have found wide utilization in several industrial applications. This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-tied photovoltaic (PV) inverters. Large installations and ambitious plans have been recently achieved for PV systems as clean and renewable power generation sources due to their improved environmental impacts and availability everywhere. Power converters represent the main parts for the grid integration of PV systems. However, PV power converters contain several power switches that construct their circuits. The power switches in PV systems are highly subjected to high stresses due to… More >