Prediction of Machine Health Condition Using Neuro-Fuzzy and Bayesian Algorithms
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[1] Ching-Hung Lee,et al. Identification and control of dynamic systems using recurrent fuzzy neural networks , 2000, IEEE Trans. Fuzzy Syst..
[2] Nando de Freitas,et al. Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.
[3] Jin Chen,et al. Neuro-fuzzy Based Condition Prediction of Bearing Health: , 2009 .
[4] Danilo P. Mandic,et al. Recurrent Neural Networks for Prediction , 2001 .
[5] Chuen-Tsai Sun,et al. Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.
[6] Simon J. Godsill,et al. On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..
[7] M. Farid Golnaraghi,et al. Prognosis of machine health condition using neuro-fuzzy systems , 2004 .
[8] Jie Liu,et al. A multi-step predictor with a variable input pattern for system state forecasting , 2009 .
[9] A. Chatterjee,et al. A Dynamical Systems Approach to Damage Evolution Tracking, Part 2: Model-Based Validation and Physical Interpretation , 2002 .
[10] George J. Vachtsevanos,et al. A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .
[11] Wilson Wang,et al. An adaptive predictor for dynamic system forecasting , 2007 .
[12] Bhaskar Saha,et al. Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.
[13] Bin Zhang,et al. Application of Blind Deconvolution Denoising in Failure Prognosis , 2009, IEEE Transactions on Instrumentation and Measurement.
[14] Danilo P. Mandic,et al. Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability , 2001 .
[15] Peter W. Tse,et al. Prediction of Machine Deterioration Using Vibration Based Fault Trends and Recurrent Neural Networks , 1999 .
[16] Xining Zhang,et al. Prediction Method of Machinery Condition Based on Recurrent Neural Networks Models , 2004 .
[17] Frank L. Lewis,et al. Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .
[18] Christophe Andrieu,et al. Sequential Monte Carlo Methods for Optimal Filtering , 2001, Sequential Monte Carlo Methods in Practice.
[19] B. Samanta,et al. Prognostics of machine condition using soft computing , 2008 .
[20] Marcos Eduardo Orchard,et al. A Particle Filtering-based Framework for On-line Fault Diagnosis and Failure Prognosis , 2007 .
[21] Bin Zhang,et al. Blind Deconvolution Denoising for Helicopter Vibration Signals , 2008, IEEE/ASME Transactions on Mechatronics.
[22] Boris Tovornik,et al. Input-output modelling with decomposed neuro-fuzzy ARX model , 2008, Neurocomputing.
[23] J. Mendel. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .
[24] Gang Niu,et al. Dempster–Shafer regression for multi-step-ahead time-series prediction towards data-driven machinery prognosis , 2009 .
[25] C. S. George Lee,et al. Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .
[26] Bo-Suk Yang,et al. Multi-step ahead direct prediction for machine condition prognosis using regression trees and neuro-fuzzy systems , 2013 .
[27] Peng Wang,et al. Fault prognostics using dynamic wavelet neural networks , 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
[28] Neil J. Gordon,et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..
[29] Daming Lin,et al. A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .