Self-organizing maps
Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison
Neural Processing Letters
Modern Digital and Analog Communication Systems 3e Osece
Modern Digital and Analog Communication Systems 3e Osece
Time Series Prediction and Neural Networks
Journal of Intelligent and Robotic Systems
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques
Empirical Software Engineering
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Context in temporal sequence processing: a self-organizing approach and its application to robotics
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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This paper presents a proactive maintenance scheme for fault detection, diagnosis and prediction in electrical valves. The proposed scheme is validated with a case study, considering a specific valve used for controlling the oil flow in a distribution network. The scheme is based in self-organizing maps, which perform fault detection and diagnosis, and temporal self-organizing maps for fault prediction. The adopted fault model considers deviations either in torque, in the valve's gate position or in the opening or closing time. The map which performs the fault detection, diagnosis and prediction, is trained with the energy spectral density information, obtained from the torque and position signals by applying the wavelet packet transform. These signals are provided by a mathematical model devised for the electrical valve. The training is performed by fault injection based on parameter deviations over this same mathematical model. The proposed system is embedded into an FPGA-based platform. Experimental results demonstrate the effectiveness of the proposed approaches.