Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Y Lei, N Li, L Guo, N Li, T Yan, J Lin - Mechanical systems and signal …, 2018‏ - Elsevier
Machinery prognostics is one of the major tasks in condition based maintenance (CBM),
which aims to predict the remaining useful life (RUL) of machinery based on condition …

Remaining useful life prediction based on a multi-sensor data fusion model

N Li, N Gebraeel, Y Lei, X Fang, X Cai, T Yan - Reliability Engineering & …, 2021‏ - Elsevier
With the rapid development of Industrial Internet of Things, more and more sensors have
been used for condition monitoring and prognostics of industrial systems. Big data collected …

Copula-based reliability analysis of degrading systems with dependent failures

G Fang, R Pan, Y Hong - Reliability Engineering & System Safety, 2020‏ - Elsevier
Consider a coherent system, in which the degradation processes of its performance
characteristics are positively correlated, this paper systematically investigates a bivariate …

Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

H Zhang, M Chen, J Shang, C Yang, Y Sun - Science China Information …, 2021‏ - Springer
Brownian motion (BM) has been widely used for degradation modeling and remaining
useful life (RUL) prediction, but it is essentially Markovian. This implies that the future state in …

A Bayesian inference for remaining useful life estimation by fusing accelerated degradation data and condition monitoring data

Z Pang, X Si, C Hu, D Du, H Pei - Reliability Engineering & System Safety, 2021‏ - Elsevier
This article addresses the problem of estimating the remaining useful life (RUL) of degrading
products by fusing the accelerated degradation data and condition monitoring (CM) data …

Big data and reliability applications: The complexity dimension

Y Hong, M Zhang, WQ Meeker - Journal of Quality Technology, 2018‏ - Taylor & Francis
Big data features not only large volumes of data but also data with complicated structures.
Complexity imposes unique challenges on big data analytics. Meeker and Hong (2014; …

Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula

F Sun, F Fu, H Liao, D Xu - Reliability Engineering & System Safety, 2020‏ - Elsevier
A modern product usually shows multiple performance characteristics that degrade
simultaneously. It is quite common that these degradation processes are dependent due to …

An artificial neural network supported stochastic process for degradation modeling and prediction

D Liu, S Wang - Reliability Engineering & System Safety, 2021‏ - Elsevier
An artificial neural network supported stochastic process for degradation modeling and
prediction is proposed in this paper. An artificial neural network is applied to describe the …

An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error

D Liu, S Wang, X Cui - Reliability Engineering & System Safety, 2022‏ - Elsevier
Due to the powerful ability of artificial neural network in data fitting, it has been applied to
describe the mean function in Wiener process for degradation modeling and estimating …

Degradation prognostics of aerial bundled cables based on multi-sensor data fusion

MU Hassan, T Khan, T Zafar, WB Yousuf… - … Testing and Evaluation, 2024‏ - Taylor & Francis
Development of advanced health monitoring sensors and high-performance computing
enabled multi-sensors information to analyse degradation in complex engineering …