Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Joint optimization of production and maintenance strategies considering a dynamic sampling strategy for a deteriorating system

(2020) Héctor Rivera-Gómez et al.   COMPUTERS & INDUSTRIAL ENGINEERING

Remaining useful life prediction using multi-scale deep convolutional neural network

(2020) Han Li et al.   APPLIED SOFT COMPUTING

Monte Carlo simulation model to coordinate the preventive maintenance scheduling of generating units in isolated distributed Power Systems

(2020) Yorlandys Salgado Duarte et al.   ELECTRIC POWER SYSTEMS RESEARCH

Remaining useful life prediction via a variational autoencoder and a time-window-based sequence neural network

(2020) Chun Su et al.   QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

Data alignments in machinery remaining useful life prediction using deep adversarial neural networks

(2020) Xiang Li et al.   KNOWLEDGE-BASED SYSTEMS

Similarity-based deep learning approach for remaining useful life prediction

(2020) Mengru Hou et al.   MEASUREMENT

Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors

(2020) Han Cheng et al.   MEASUREMENT

Maintenance grouping optimization with system multi-level information based on BN lifetime prediction model

(2019) Xiaohong Wang et al.   JOURNAL OF MANUFACTURING SYSTEMS

Exact and heuristic approaches for joint maintenance and spare parts planning

(2019) Pınar Bülbül et al.   COMPUTERS & INDUSTRIAL ENGINEERING

Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method

(2019) Yawei Hu et al.   Chinese Journal of Mechanical Engineering

Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network

(2019) Jun Wu et al.   ISA TRANSACTIONS

Developing a novel risk-based MCDM approach based on D numbers and fuzzy information axiom and its applications in preventive maintenance planning

(2019) Hamidreza Seiti et al.   APPLIED SOFT COMPUTING

Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery

(2019) Biao Wang et al.   NEUROCOMPUTING

A novel deep learning method based on attention mechanism for bearing remaining useful life prediction

(2019) Yuanhang Chen et al.   APPLIED SOFT COMPUTING

Impact of condition based maintenance policies on the service level of multi-stage manufacturing systems

(2018) Alessio Angius et al.   CONTROL ENGINEERING PRACTICE

Estimation of active maintenance opportunity windows in Bernoulli production lines

(2017) Xi Gu et al.   JOURNAL OF MANUFACTURING SYSTEMS

The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance

(2017) Bram de Jonge et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

Methodologies for system-level remaining useful life prediction

(2016) Hamed Khorasgani et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

(2015) Gian Antonio Susto et al.   IEEE Transactions on Industrial Informatics

A Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques

(2012) Gian Antonio Susto et al.   IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING

Remaining useful life estimation – A review on the statistical data driven approaches

(2010) Xiao-Sheng Si et al.   EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Prognostic modelling options for remaining useful life estimation by industry

(2010) J.Z. Sikorska et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Maintenance practices in Swedish industries: Survey results

(2009) Imad Alsyouf   INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS

Integrated production planning and preventive maintenance in deteriorating production systems

(2008) El-Houssaine Aghezzaf et al.   INFORMATION SCIENCES

Throughput analysis of production systems: recent advances and future topics

(2008) Jingshan Li et al.   INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH