On the public citation graph

Predictive Maintenance Strategy Based on Conservative Remaining Life Prediction

0 signed · 36 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10479-023-05652-4openalex-snapshot
A poverty index prediction model for students based on PSO-LightGBM
inferredcites →https://doi.org/10.1016/0169-2070(89)90070-8openalex-snapshot
Improving a group forecast by removing the conservative bias in its components
inferredcites →https://doi.org/10.1016/j.cie.2024.110581openalex-snapshot
Multi-objective predictive maintenance scheduling models integrating remaining useful life prediction and maintenance decisions
inferredcites →https://doi.org/10.1016/j.cirpj.2022.11.004openalex-snapshot
Challenges in predictive maintenance – A review
inferredcites →https://doi.org/10.1016/j.egyr.2021.08.182openalex-snapshot
A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.ejor.2014.11.018openalex-snapshot
Which is better for replacement policies with continuous or discrete scheduled times?
inferredcites →https://doi.org/10.1016/j.enconman.2019.112239openalex-snapshot
Short-term average wind speed and turbulent standard deviation forecasts based on one-dimensional convolutional neural network and the integrate method for probabilistic framework
inferredcites →https://doi.org/10.1016/j.jbusres.2015.03.031openalex-snapshot
Golden rule of forecasting: Be conservative
inferredcites →https://doi.org/10.1016/j.jclepro.2020.120813openalex-snapshot
A review of the state of health for lithium-ion batteries: Research status and suggestions
inferredcites →https://doi.org/10.1016/j.jmsy.2020.07.008openalex-snapshot
Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics
inferredcites →https://doi.org/10.1016/j.jmsy.2024.08.025openalex-snapshot
An aircraft engine remaining useful life prediction method based on predictive vector angle minimization and feature fusion gate improved transformer model
inferredcites →https://doi.org/10.1016/j.neucom.2022.04.055openalex-snapshot
Data-driven predictive maintenance strategy considering the uncertainty in remaining useful life prediction
inferredcites →https://doi.org/10.1016/j.patcog.2017.10.013openalex-snapshot
Recent advances in convolutional neural networks
inferredcites →https://doi.org/10.1016/j.ress.2014.09.024openalex-snapshot
A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes
inferredcites →https://doi.org/10.1016/j.ress.2017.01.011openalex-snapshot
Age replacement models: A summary with new perspectives and methods
inferredcites →https://doi.org/10.1016/j.ress.2019.03.018openalex-snapshot
A new dynamic predictive maintenance framework using deep learning for failure prognostics
inferredcites →https://doi.org/10.1016/j.ress.2020.107052openalex-snapshot
An integrated assessment of safety and efficiency of aircraft maintenance strategies using agent-based modelling and stochastic Petri nets
inferredcites →https://doi.org/10.1016/j.ress.2021.107713openalex-snapshot
Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning
inferredcites →https://doi.org/10.1016/j.ress.2021.107927openalex-snapshot
Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction
inferredcites →https://doi.org/10.1016/j.ress.2022.108869openalex-snapshot
Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models
inferredcites →https://doi.org/10.1016/j.ress.2023.109096openalex-snapshot
An integrated multi-head dual sparse self-attention network for remaining useful life prediction
inferredcites →https://doi.org/10.1016/j.ress.2024.110012openalex-snapshot
Dynamic predictive maintenance strategy for system remaining useful life prediction via deep learning ensemble method
inferredcites →https://doi.org/10.1016/j.ress.2024.110394openalex-snapshot
Data augmentation based on diffusion probabilistic model for remaining useful life estimation of aero-engines
inferredcites →https://doi.org/10.1016/j.ress.2025.111289openalex-snapshot
Dynamic predictive maintenance strategy for the multi-state system based on remaining life prediction
inferredcites →https://doi.org/10.1016/j.ymssp.2017.11.024openalex-snapshot
A review on the application of deep learning in system health management
inferredcites →https://doi.org/10.1016/j.ymssp.2019.05.005openalex-snapshot
Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme
inferredcites →https://doi.org/10.1080/24725854.2020.1766729openalex-snapshot
A Bayesian deep learning framework for interval estimation of remaining useful life in complex systems by incorporating general degradation characteristics
inferredcites →https://doi.org/10.1108/jqme-05-2018-0039openalex-snapshot
A case-based reasoning system for fault detection and isolation: a case study on complex gearboxes
inferredcites →https://doi.org/10.1109/jas.2021.1003835openalex-snapshot
A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance
inferredcites →https://doi.org/10.1109/jiot.2021.3055977openalex-snapshot
Remaining Useful Life Prediction of IIoT-Enabled Complex Industrial Systems With Hybrid Fusion of Multiple Information Sources
inferredcites →https://doi.org/10.1109/jsen.2021.3119553openalex-snapshot
Dynamic Predictive Maintenance Scheduling Using Deep Learning Ensemble for System Health Prognostics
inferredcites →https://doi.org/10.1109/phm.2008.4711414openalex-snapshot
Damage propagation modeling for aircraft engine run-to-failure simulation
inferredcites →https://doi.org/10.1109/tie.2020.2972443openalex-snapshot
Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach
inferredcites →https://doi.org/10.1109/tr.2012.2182818openalex-snapshot
Optimization Problems of a Parallel System With a Random Number of Units
inferredcites →https://doi.org/10.1109/tr.2013.2285318openalex-snapshot
Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression
inferredcites →https://doi.org/10.1609/aaai.v35i12.17325openalex-snapshot
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.