☆ 4.7 Article

Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures

RELIABILITY ENGINEERING & SYSTEM SAFETY (2024)

Related references

Note: Only part of the references are listed.

Article Engineering, Industrial

A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system

Zhenan Pang et al.

Summary: This paper proposes a condition-based prognostic approach for age-and state-dependent partially observable nonlinear degrading system. The proposed approach characterizes the dynamics and nonlinearity of the system degradation process using age-and state-dependent nonlinear diffusion process and state space model. The distribution of the remaining useful life is derived using extended Kalman filtering and expectation-maximization algorithm, and can be updated in real-time with new data.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2023)

Add to Collection

Article Engineering, Industrial

Degradation modeling and RUL prediction with Wiener process considering measurable and unobservable external impacts

Shuyi Zhang et al.

Summary: This study incorporates both measurable covariates and unobservable factors into degradation modeling using the Wiener process. The impacts of the covariates and unobservable factors on degradation rate are accounted for. Maximum likelihood estimation and simulation-based algorithm are used for remaining useful life prediction. The proposed model is validated through simulation study and applied to battery degradation and outdoor coating weathering datasets.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2023)

Add to Collection

Article Engineering, Industrial

A prognostic driven predictive maintenance framework based on Bayesian deep learning

Liangliang Zhuang et al.

Summary: Recent years have seen significant advances in predictive maintenance (PdM) for complex industrial systems. However, existing literature primarily focuses on either prognostics or maintenance decision making, without integrating the two stages. In this paper, we propose a dynamic PdM framework that integrates prognostics and maintenance decision making, using a Bayesian deep learning model to characterize the relationship between degradation features and remaining useful life (RUL). The framework can generate a predictive RUL distribution that effectively represents prognostic uncertainties, and update maintenance decisions based on the latest predictive RUL information.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2023)

Add to Collection

Article Mechanics

A novel strain-based health indicator for the remaining useful life estimation of degrading composite structures

Georgios Galanopoulos et al.

Summary: We propose a generic methodology for developing a Health Indicator from strain-based Structural Health Monitoring data, which is suitable for prognostic tasks. An in-house test campaign is conducted to validate the proposed method, using single-stringered composite panels subjected to different fatigue scenarios. A fusion scheme based on Genetic Algorithms is introduced, resulting in a fused Health Indicator with high monotonicity and prognosability. Gaussian Process Regression is employed to predict the Remaining Useful Life of the panels, and the newly proposed Health Indicator performs more accurately than traditional methods.

COMPOSITE STRUCTURES (2023)

Add to Collection

Article Engineering, Industrial

Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis

Huixing Meng et al.

Summary: This paper proposes a novel method for battery prognostics using random segments of charging curves, achieving accurate state of health (SOH) estimation. The method utilizes partial incremental capacity analysis and a long short-term memory network guided by Bayesian optimization.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2023)

Add to Collection

Proceedings Paper Engineering, Civil

An SHM Data-Driven Methodology for the Remaining Useful Life Prognosis of Aeronautical Subcomponents

Georgios Galanopoulos et al.

Summary: In this study, a methodology for predicting the remaining useful life (RUL) of aerospace components is proposed. Strain measurements and health indicators are used to capture the degradation process, and a fusion approach is employed for RUL estimation.

EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 1 (2023)

Add to Collection

Article Engineering, Industrial

An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation

Wennian Yu et al.

Summary: This paper introduces a generalized Wiener process-based degradation model with an adaptive drift to characterize complex degradation behaviors. A recursive Bayesian filtering algorithm is used to update the drift distribution and an expectation-maximization algorithm is utilized for online estimation of model parameters. An analytical approximation of the Remaining Useful Life distribution is derived and validated using a practical milling dataset, demonstrating superior performance compared to existing methods.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2022)

Add to Collection

Article Automation & Control Systems

Heterogeneous bi-directional recurrent neural network combining fusion health indicator for predictive analytics of rotating machinery

Yuanhong Chang et al.

Summary: This paper proposes a data-driven intelligent method for predicting the operational reliability and natural degradation of rotating machinery. The method constructs a support evidence space, integrates evidence features from multiple domains, and designs a hybrid prediction network to improve prediction accuracy.

ISA TRANSACTIONS (2022)

Add to Collection

Article Engineering, Industrial

Efficient temporal flow Transformer accompanied with multi-head probsparse self-attention mechanism for remaining useful life prognostics

Yuanhong Chang et al.

Summary: This paper proposes an efficient Temporal Flow Transformer (TFT) for RUL prognostics of rolling bearings. The TFT extracts effective degradation features from raw signals using specially designed multi-head sparse self-attention mechanism and is trained by a knowledge-induced distillation strategy. The extensive experiments show that the TFT outperforms existing state-of-the-art methods and provides a new solution for RUL prognostics.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2022)

Add to Collection

Article Automation & Control Systems

An automated health indicator construction methodology for prognostics based on multi-criteria optimization

Khanh T. P. Nguyen et al.

Summary: This paper introduces a new methodology for developing autonomous health management systems, aiming to automatically select relevant measurements from data sources, extract useful features from raw data obtained from high-frequency sensors, and create the most appropriate health indicators. The proposed method, utilizing genetic programming flexibility, allows for easy integration of information on system degradation trends without requiring expertise knowledge, and has been validated in two real application case studies.

ISA TRANSACTIONS (2021)

Add to Collection

Article Engineering, Industrial

A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings

Yudong Cao et al.

Summary: The study introduces a new deep learning framework TCN-RSA for predicting the remaining useful life of mechanical systems, and demonstrates its superiority in experiments.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)

Add to Collection

Article Engineering, Industrial

Remaining useful life prediction for multi-phase deteriorating process based on Wiener process

Guobo Liao et al.

Summary: The paper presents a multi-phase degradation model based on Wiener process and uses a Bayesian approach to integrate historical and real-time data for remaining useful life prediction. By estimating hyperparameters using the expectation maximization algorithm, model parameters can be effectively updated to account for multiple random change points.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)

Add to Collection

Article Engineering, Industrial

Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process

Sen-Ju Zhang et al.

Summary: This paper proposes a systematic method for degradation modeling and remaining useful life prediction based on uncertain process, with a novel similarity-based uncertain weighted least squares estimation method and denoising technique for recovery noises. The effectiveness of the proposed method is illustrated through a case study on real lithium-ion battery degradation dataset in comparison with traditional stochastic processes.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)

Add to Collection

Article Chemistry, Analytical

Health Monitoring of Aerospace Structures Utilizing Novel Health Indicators Extracted from Complex Strain and Acoustic Emission Data

Georgios Galanopoulos et al.

Summary: The study developed new damage-sensitive features from acquired Fiber Bragg Grating (FBG) and acoustic emission (AE) data for health monitoring of composite structures. By employing appropriate sensors in fatigue tests on composite panels, the developed health indicators showed promising behaviors with moderate prognosability. Monitoring damage growth and verifying with phased array ultrasound, the study attempted a correlation with actual damage throughout the experiments.

SENSORS (2021)

Add to Collection

Article Mechanics

An adaptive probabilistic data -driven methodology for prognosis of the fatigue life of composite structures

Nick Eleftheroglou et al.

COMPOSITE STRUCTURES (2020)

Add to Collection

Article Automation & Control Systems

Valve Failure Prognostics in Reciprocating Compressors Utilizing Temperature Measurements, PCA-Based Data Fusion, and Probabilistic Algorithms

Theodoros Loutas et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

Add to Collection

Article Engineering, Industrial

Structural health monitoring data fusion for in-situ life prognosis of composite structures

Nick Eleftheroglou et al.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2018)

Add to Collection

Article Computer Science, Information Systems

Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine

Faisal Khan et al.

DATA (2018)

Add to Collection

Article Chemistry, Physical

Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks

Claudio Sbarufatti et al.

JOURNAL OF POWER SOURCES (2017)

Add to Collection

Article Mechanics

A data-driven probabilistic framework towards the in-situ prognostics of fatigue life of composites based on acoustic emission data

Theodoros Loutas et al.

COMPOSITE STRUCTURES (2017)

Add to Collection

Article Engineering, Multidisciplinary

Fatigue damage diagnostics and prognostics of composites utilizing structural health monitoring data and stochastic processes

Nick Eleftheroglou et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2016)

Add to Collection

Article Automation & Control Systems

Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction

Linxia Liao

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2014)

Add to Collection

Article Engineering, Industrial

Hazard rate function in dynamic environment

Lu XiaoFei et al.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2014)

Add to Collection

Article Engineering, Industrial

An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process

Ramin Moghaddass et al.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2014)

Add to Collection

Article Chemistry, Analytical

A Monotonic Degradation Assessment Index of Rolling Bearings Using Fuzzy Support Vector Data Description and Running Time

Zhongjie Shen et al.

SENSORS (2012)

Add to Collection

Article Engineering, Multidisciplinary

Development of method for in-service crack detection based on distributed fiber optic sensors

Branko Glisic et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2012)

Add to Collection

Article Engineering, Mechanical

A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction

Ying Peng et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2011)

Add to Collection

Article Engineering, Electrical & Electronic

A model for residual life prediction based on Brownian motion with an adaptive drift

Wenbin Wang et al.

MICROELECTRONICS RELIABILITY (2011)

Add to Collection

Article Automation & Control Systems

A particle-filtering approach for on-line fault diagnosis and failure prognosis

Marcos E. Orchard et al.

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL (2009)

Add to Collection

Article Automation & Control Systems

Genetically programmed-based artificial features extraction applied to fault detection

Hiram Firpi et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2008)

Add to Collection

Article Engineering, Industrial

Residual-life distributions from component degradation signals: A Bayesian approach

NZ Gebraeel et al.

IIE TRANSACTIONS (2005)

Add to Collection

© Peeref 2019-2024. All rights reserved.