Degradation modeling applied to residual lifetime prediction using functional data analysis

DOI10.1214/10-AOAS448zbMath1223.62156arXiv1107.5712MaRDI QIDQ641105

Nagi Gebraeel, Rensheng R. Zhou, Nicoleta Serban

Publication date: 21 October 2011

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1107.5712

zbMATH Keywords

nonparametric estimationfunctional principal component analysiscondition monitoringresidual life distributionsparse degradation signal

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Empirical decision procedures; empirical Bayes procedures (62C12) Reliability and life testing (62N05)

Related Items (7)

Remaining useful life prediction: A multiple product partition approach ⋮ Dynamical functional prediction and classification, with application to traffic flow prediction ⋮ Optimal classification policy and comparisons for highly reliable products ⋮ Degradation modeling applied to residual lifetime prediction using functional data analysis ⋮ Degradation-based residual life prediction under different environments ⋮ A Wiener process model with truncated normal distribution for reliability analysis ⋮ The latent state hazard model, with application to wind turbine reliability

Uses Software

• fda (R)
• bootstrap
• bootlib

Cites Work

• Degradation modeling applied to residual lifetime prediction using functional data analysis
• Functional data analysis
• Inference from accelerated degradation and failure data based on Gaussian process models
• Nonlinear time series. Nonparametric and parametric methods
• Time-Varying Functional Regression for Predicting Remaining Lifetime Distributions from Longitudinal Trajectories
• Accelerated Testing
• Bayesian analysis for inverse gaussian lifetime data with measures of degradation
• Principal component models for sparse functional data
• Using Degradation Measures to Estimate a Time-to-Failure Distribution
• Functional Data Analysis for Sparse Longitudinal Data
• Models for Variable-Stress Accelerated Life Testing Experiments Based on Wiener Processes and the Inverse Gaussian Distribution
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