J
Jianguo Wu
Researcher at Peking University
Publications - 43
Citations - 440
Jianguo Wu is an academic researcher from Peking University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 9, co-authored 29 publications receiving 247 citations. Previous affiliations of Jianguo Wu include University of Texas at El Paso & Purdue University.
Papers
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Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity
TL;DR: A multiple change-point Wiener process as a degradation model is proposed to better characterize the degradation signals of multiple-phase characteristics and a fully Bayesian approach is developed where all model parameters are assumed random.
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Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction
Yuxin Wen,Jianguo Wu,Yuan Yuan +2 more
TL;DR: A flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis and a particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction.
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Online Steady-State Detection for Process Control Using Multiple Change-Point Models and Particle Filters
TL;DR: A robust online steady-state detection algorithm using multiple change-point model and particle filtering techniques is proposed, which is more accurate and robust than the other existing methods.
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A new fuzzy c-means clustering-based time series segmentation approach and its application on tunnel boring machine analysis
TL;DR: The proposed fuzzy c-means clustering-based time series segmentation approach for TBM time series data shows that it can accurately identify different excavation status of the TBM, and help the other data mining tasks of TBM as well.
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A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction
TL;DR: A joint prognostic model (JPM) is proposed, where Bayesian linear models are developed for multisensor data, and an artificial neural network is proposed to model the nonlinear relationship between the residual life, the model parameters of each sensorData, and the observation epoch.