专题：Reliability and Maintenance Optimization

This cluster of papers focuses on reliability engineering and maintenance optimization, covering topics such as degradation modeling, condition-based maintenance, multi-state systems, prognostic models, accelerated degradation tests, risk-based maintenance, stochastic modeling, and system reliability. The papers explore various methods and strategies for optimizing maintenance policies and improving the reliability of deteriorating systems.
最新文献
Statistical Multivariate Degradation Modeling-A Systematic Review

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Optimal test termination time in reliability growth management for systems with multiple failure modes

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Multi-task learning framework for fault detection in energy storage system lithium-ion batteries: From degradation to slight overcharge

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Advanced health management of lithium-ion batteries: Life extension using bidirectional pulse current regulation

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Bayesian Study of a Weibull–Dhillon Competing Risk Model and Its Reliability Implications via HMC Sampling Methodology

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A hybrid machine learning algorithm approach to predictive maintenance tasks: a comparison with machine learning algorithms

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Operational risk-based opportunistic maintenance approach for multi-station manufacturing systems considering machine performance–workpiece quality co-effect

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Capacity and State-of-Health Prediction of Lithium-Ion Batteries Using Reduced Equivalent Circuit Models

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Joint optimization of condition-based operation and maintenance strategy for load-sharing systems subject to hybrid continuous and discrete loads

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A new flexible power-X family of distributions: Applications to reliability engineering data

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近5年高被引文献
Offshore wind turbine operations and maintenance: A state-of-the-art review

review Full Text OpenAlex 412 FWCI17.459

Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries

article Full Text OpenAlex 359 FWCI34.565

Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry

article Full Text OpenAlex 338 FWCI32.896

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

article Full Text OpenAlex 251 FWCI25.907

On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges

article Full Text OpenAlex 225 FWCI104.13

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries

review Full Text OpenAlex 215 FWCI3.912

Prognostics and health management: A review from the perspectives of design, development and decision

review Full Text OpenAlex 213 FWCI8.674

Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data

article Full Text OpenAlex 202 FWCI15.619

Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

article Full Text OpenAlex 199 FWCI19.597

Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

article Full Text OpenAlex 196 FWCI33.292