人口
降级(电信)
计算机科学
贝叶斯概率
参数化模型
参数统计
可靠性(半导体)
鉴定(生物学)
数据挖掘
机器学习
人工智能
统计
数学
生物
电信
量子力学
物理
社会学
人口学
功率(物理)
植物
作者
Hung T. Nguyen,Xuxue Sun,Qing Lu,Qiong Zhang,Mingyang Li
摘要
Abstract Successful modeling of degradation data is of great importance for both accurate reliability assessment and effective maintenance decision‐making. Many of existing degradation performance modeling approaches either assume a homogeneous population of units or characterize a heterogeneous population with some restrictive assumptions, such as pre‐specifying the number of sub‐populations. This paper proposes a Bayesian heterogeneous degradation performance modeling framework to relax the conventional modeling assumptions. Specifically, a Bayesian non‐parametric model formulation and learning algorithm are proposed to characterize the historical degradation data of a heterogeneous population of units with an unknown number of homogeneous sub‐populations and allowing the joint model estimation and sub‐population number identification. Based on the off‐line population‐level model, an on‐line individual‐level degradation model with sequential model updating is further developed to improve remaining useful life prediction of individual units with sparse data. A real case study using the heterogeneous degradation data of deteriorating roads is provided to illustrate the proposed approach and demonstrate its validity.
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