Analysis and classification of faults in switched reluctance motors using deep learning neural networks
E Fantin Irudaya Raj, M Balaji - Arabian Journal for Science and …, 2021 - Springer
The simple and robust construction, less weight, wide operating speed range, and higher
fault tolerance capability of switched reluctance (SR) motor make it a viable contender for …
fault tolerance capability of switched reluctance (SR) motor make it a viable contender for …
Intelligent pattern recognition of a SLM machine process and sensor data
E Uhlmann, RP Pontes, A Laghmouchi, A Bergmann - Procedia Cirp, 2017 - Elsevier
Abstract Selective Laser Melting is an additive manufacturing process, in which the research
has been increasing over the past few years to meet customer-specific requirements …
has been increasing over the past few years to meet customer-specific requirements …
Neuro-heuristic computational intelligence for solving nonlinear pantograph systems
We present a neuro-heuristic computing platform for finding the solution for initial value
problems (IVPs) of nonlinear pantograph systems based on functional differential equations …
problems (IVPs) of nonlinear pantograph systems based on functional differential equations …
Correction of array failure using grey wolf optimizer hybridized with an interior point algorithm
We design a grey wolf optimizer hybridized with an interior point algorithm to correct a faulty
antenna array. If a single sensor fails, the radiation power pattern of the entire array is …
antenna array. If a single sensor fails, the radiation power pattern of the entire array is …
Automated identification and assessment of environmental noise sources
Noise pollution is one of the major health risks in urban life. The approach to measurement
and identification of noise sources needs to be improved and enhanced to reduce high …
and identification of noise sources needs to be improved and enhanced to reduce high …
Real‐Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network
Low power hub motors are widely used in electromechanical systems such as electrical
bicycles and solar vehicles due to their robustness and compact structure. Such systems …
bicycles and solar vehicles due to their robustness and compact structure. Such systems …
Active fault diagnosis of a switched reluctance motor using sliding mode observer and average torque estimator for light electric vehicle applications
MU Jamil, W Kongprawechnon… - … on Electrical Energy …, 2020 - Wiley Online Library
In this study, an active fault diagnosis of a switched reluctance motor (SRM) using a sliding
mode observer (SMO) and an average torque estimator for light electric vehicle (LEV) is …
mode observer (SMO) and an average torque estimator for light electric vehicle (LEV) is …
Design and implementation of SOC prediction for a Li-Ion battery pack in an electric car with an embedded system
Li-Ion batteries are widely preferred in electric vehicles. The charge status of batteries is a
critical evaluation issue, and many researchers are studying in this area. State of charge …
critical evaluation issue, and many researchers are studying in this area. State of charge …
An embedded controller application with regenerative braking for the electric vehicle
Y Karabacak, A Uysal - Elektronika ir Elektrotechnika, 2020 - eejournal.ktu.lt
Regenerative braking is very important for increasing the total range of an electric vehicle. In
this study, an embedded controller, including regenerative braking, is designed and …
this study, an embedded controller, including regenerative braking, is designed and …
Real time determination of rechargeable batteries' type and the state of charge via cascade correlation neural network
Batteries are used to store electrical energy as chemical energy. They have a wide using
area from portable equipment to electric vehicles. It is important to know the state of charge …
area from portable equipment to electric vehicles. It is important to know the state of charge …