An Intelligent Prognostic System for Gear Performance Degradation Assessment and Remaining Useful Life Estimation

[1]  Peter W. Tse,et al.  A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features , 2015 .

[2]  Nan Chen,et al.  The Inverse Gaussian Process as a Degradation Model , 2014, Technometrics.

[3]  Ming J. Zuo,et al.  Multistate degradation and supervised estimation methods for a condition-monitored device , 2014 .

[4]  Qiang Miao,et al.  Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model , 2013 .

[5]  Kwok-Leung Tsui,et al.  An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..

[6]  Wei Liang,et al.  Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..

[7]  Ping Yang,et al.  Random Vibration Analysis of Planetary Gear Trains , 2013 .

[8]  Stanislav Ziaran,et al.  Determination of the State of Wear of High Contact Ratio Gear Sets by Means of Spectrum and Cepstrum Analysis , 2013 .

[9]  Ming Jian Zuo,et al.  A parameter estimation method for a condition-monitored device under multi-state deterioration , 2012, Reliab. Eng. Syst. Saf..

[10]  Grzegorz Litak,et al.  Failure Diagnosis of a Gear Box by Recurrences , 2012 .

[11]  Michael Pecht,et al.  Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance , 2012 .

[12]  Michael Osterman,et al.  Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .

[13]  Ying Peng,et al.  A hybrid approach of HMM and grey model for age-dependent health prediction of engineering assets , 2011, Expert Syst. Appl..

[14]  Bin Zhang,et al.  Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering , 2011, IEEE Transactions on Industrial Electronics.

[15]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[16]  Ming Liang,et al.  Detection and diagnosis of bearing and cutting tool faults using hidden Markov models , 2011 .

[17]  Enrico Zio,et al.  Particle filtering prognostic estimation of the remaining useful life of nonlinear components , 2011, Reliab. Eng. Syst. Saf..

[18]  Peter W. Tse,et al.  Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis , 2011 .

[19]  Michael Pecht,et al.  A probabilistic description scheme for rotating machinery health evaluation , 2010 .

[20]  Ming Yang,et al.  ARX model-based gearbox fault detection and localization under varying load conditions , 2010 .

[21]  Ming Yang,et al.  A wavelet approach to fault diagnosis of a gearbox under varying load conditions , 2010 .

[22]  Gang Niu,et al.  Machine condition prognosis based on sequential Monte Carlo method , 2010, Expert Syst. Appl..

[23]  Chris K. Mechefske,et al.  Gearbox vibration monitoring using extended Kalman filters and hypothesis tests , 2009 .

[24]  Dong Wang,et al.  Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition , 2009 .

[25]  Enrico Zio,et al.  Monte Carlo-based filtering for fatigue crack growth estimation , 2009 .

[26]  Yimin Zhan,et al.  Robust detection of gearbox deterioration using compromised autoregressive modeling and Kolmogorov–Smirnov test statistic—Part I: Compromised autoregressive modeling with the aid of hypothesis tests and simulation analysis , 2007 .

[27]  David He,et al.  A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology , 2007 .

[28]  Yimin Zhan,et al.  Robust detection of gearbox deterioration using compromised autoregressive modeling and Kolmogorov–Smirnov test statistic. Part II: Experiment and application , 2007 .

[29]  K. Loparo,et al.  Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling : A method for bearing prognostics , 2007 .

[30]  Qiang Miao,et al.  Singularity detection in machinery health monitoring using Lipschitz exponent function , 2007 .

[31]  David He,et al.  Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis , 2007, Eur. J. Oper. Res..

[32]  V. Makis,et al.  Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models , 2007 .

[33]  M.G. Pecht,et al.  Prognostics and health management of electronics , 2008, IEEE Transactions on Components and Packaging Technologies.

[34]  Ming Dong,et al.  Equipment health diagnosis and prognosis using hidden semi-Markov models , 2006 .

[35]  Fulei Chu,et al.  Fault recognition method for speed-up and speed-down process of rotating machinery based on independent component analysis and Factorial Hidden Markov Model , 2006 .

[36]  Viliam Makis,et al.  A robust diagnostic model for gearboxes subject to vibration monitoring , 2006 .

[37]  Viliam Makis,et al.  Adaptive state detection of gearboxes under varying load conditions based on parametric modelling , 2006 .

[38]  Daming Lin,et al.  An approach to signal processing and condition-based maintenance for gearboxes subject to tooth failure , 2004 .

[39]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[40]  Kenneth A. Loparo,et al.  Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs) , 2001 .

[41]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .

[42]  P. D. McFadden,et al.  Decomposition of gear motion signals and its application to gearbox diagnostics , 1995 .

[43]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.