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Haitao Liao, Ph.D.

Affiliations: 
2004 Rutgers University, New Brunswick, New Brunswick, NJ, United States 
Area:
Industrial Engineering
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"Haitao Liao"
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Elsayed A. Elsayed grad student 2004 Rutgers, New Brunswick
 (Degradation models and design of accelerated degradation testing plans.)

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Publications

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Wang H, Liao H, Ma X. (2020) Remaining Useful Life Prediction Considering Joint Dependency of Degradation Rate and Variation on Time-Varying Operating Conditions Ieee Transactions On Reliability. 1-14
Sun F, Fu F, Liao H, et al. (2020) Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula Reliability Engineering & System Safety. 204: 107168
Wang N, Hu J, Ma L, et al. (2020) Availability Analysis and Preventive Maintenance Planning for Systems with General Time Distributions Reliability Engineering & System Safety. 201: 106993
Ma Z, Wang S, Ruiz C, et al. (2020) Reliability estimation from two types of accelerated testing data considering measurement error Reliability Engineering & System Safety. 193: 106610
Zhang X, Ni W, Liao H, et al. (2020) Improved condition monitoring for an FPSO system with multiple correlated components Measurement. 166: 108223
Song Y, Liu D, Liao H, et al. (2020) A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries Applied Energy. 261: 114408
Liu X, Yang T, Pei J, et al. (2019) Replacement and inventory control for a multi-customer product service system with decreasing replacement costs European Journal of Operational Research. 273: 561-574
Ruiz C, Pohl EA, Liao H, et al. (2019) A Bayesian framework for accelerated reliability growth testing with multiple sources of uncertainty Quality and Reliability Engineering International. 35: 837-853
Li X, Chen W, Sun F, et al. (2018) Bayesian accelerated acceptance sampling plans for a lognormal lifetime distribution under Type-I censoring Reliability Engineering & System Safety. 171: 78-86
Zhang Y, Wang L, Wang S, et al. (2018) Auxiliary power unit failure prediction using quantified generalized renewal process Microelectronics Reliability. 84: 215-225
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