A forecasting model of fuzzy self-regression
Fuzzy Sets and Systems
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Blind Equalization of Nonlinear Communication Channels Using Recurrent Wavelet Neural Networks
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Wavelet neural networks for eeg modeling and classification
Wavelet neural networks for eeg modeling and classification
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Machine condition prognosis based on sequential Monte Carlo method
Expert Systems with Applications: An International Journal
WSEAS Transactions on Systems and Control
Equipment PHM using non-stationary segmental hidden semi-Markov model
Robotics and Computer-Integrated Manufacturing
Bearing fault prognosis based on health state probability estimation
Expert Systems with Applications: An International Journal
A multiwavelet support vector machine prediction algorithm for avionics PHM
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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Modern industry is concerned about extending the lifetime of its critical processes and maintaining them only when required. Significant aspects of these trends include the ability to diagnose impending failures, prognosticate the remaining useful lifetime of the process and schedule maintenance operations so that uptime is maximized. Prognosis is probably the most difficult of the three issues leading to condition-based maintenance (CBM). This paper attempts to address this challenging problem with intelligence-oriented techniques, specifically dynamic wavelet neural networks (DWNNs). DWNNs incorporate temporal information and storage capacity into their functionality so that they can predict into the future, carrying out fault prognostic tasks. Such fundamental issues as the network structure, learning algorithms, stability analysis, uncertainty management, and performance assessment are studied in a theoretical framework. An example is presented in which a trained DWNN successfully prognoses a defective bearing with a crack in its inner race.