Review of neural networks for speech recognition
Neural Computation
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Diagnosing Dynamic Faults Using Modular Neural Nets
IEEE Expert: Intelligent Systems and Their Applications
Artificial Neural Networks
Hopfield/ART-1 neural network-based fault detection and isolation
IEEE Transactions on Neural Networks
Fault diagnosis of pneumatic systems with artificial neural network algorithms
Expert Systems with Applications: An International Journal
International Journal of Data Analysis Techniques and Strategies
Vibration based fault diagnosis of monoblock centrifugal pump using decision tree
Expert Systems with Applications: An International Journal
Predicting remaining useful life of rotating machinery based artificial neural network
Computers & Mathematics with Applications
International Journal of Data Analysis Techniques and Strategies
Wavelet decomposition and support vector machine for fault diagnosis of monoblock centrifugal pump
International Journal of Data Analysis Techniques and Strategies
Soft computing approach to fault diagnosis of centrifugal pump
Applied Soft Computing
Neural network based model for fault diagnosis of pneumatic valve with dimensionality reduction
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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The detection and diagnosis of faults in technical systems are of great practical significance and paramount importance for the safe operation of the plant. An early detection of faults may help to avoid product deterioration, performance degradation, major damage to the machinery itself and damage to human health or even loss of lives. The centrifugal pumping rotary system is considered for this research. This paper presents the development of artificial neural network-based model for the fault detection of centrifugal pumping system. The fault detection model is developed by using two different artificial neural network approaches, namely feed forward network with back propagation algorithm and binary adaptive resonance network (ART1). The training and testing data required are developed for the neural network model that were generated at different operating conditions, including fault condition of the system by real-time simulation through experimental model. The performance of the developed back propagation and ART1 model were tested for a total of seven categories of faults in the centrifugal pumping system. The results are compared and the conclusions are presented.