Journal ArticleDOI
Knowledge-based data augmentation of small samples for oil condition prediction
TLDR
The high predicted accuracy demonstrates that the reliability of oil condition prediction can be guaranteed even with small samples, and the proposed data augmentation method is proposed for improved prediction by integrating degradation mechanisms and monitoring data.About:
This article is published in Reliability Engineering & System Safety.The article was published on 2022-01-01. It has received 10 citations till now. The article focuses on the topics: Reliability (semiconductor) & Degradation (telecommunications).read more
Citations
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Journal ArticleDOI
ACGAN and BN based method for downhole incident diagnosis during the drilling process with small sample data size
TL;DR: In this article , the authors proposed a new method for the diagnosis of downhole drilling incidents based on an Auxiliary Classifier Generative Adversarial Networks (ACGAN) and an incident diagnosis model is built using the Bayesian network (BN).
Journal ArticleDOI
Multiattribute Modeling for Oil Condition Assessment Considering Uncertainties
TL;DR: In this article , a knowledge-guided three-layer model is established for characterizing the multi-attribute oil state, and data dispersion is considered by assigning fuzzy probability among the attribute layer.
Journal ArticleDOI
Adaptive Evolution Enhanced Physics-Informed Neural Networks for Time-Variant Health Prognosis of Lithium-Ion Batteries
Journal ArticleDOI
Adaptive evolution enhanced physics-informed neural networks for time-variant health prognosis of lithium-ion batteries
TL;DR: In this article , an adaptive evolution enhanced physics-informed neural network-based time-variant health prognosis framework for lithium-ion batteries is presented for the safe and stable long-term operation of electric equipment.
Journal ArticleDOI
Remaining Useful Life Prediction of Lubricating Oil With Small Samples
TL;DR: In this paper , a novel RUL prediction model is developed based on the Wiener process and the oil degradation mechanism, where data augmentation is adopted to enhance data quantity for reliable nonlinear parameter estimation.
References
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Journal ArticleDOI
On sequential Monte Carlo sampling methods for Bayesian filtering
TL;DR: An overview of methods for sequential simulation from posterior distributions for discrete time dynamic models that are typically nonlinear and non-Gaussian, and how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature are shown.
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Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood
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Interpolating, extrapolating, and comparing incidence-based species accumulation curves
TL;DR: In this paper, a binomial mixture model is proposed for the species accumulation function based on presence-absence (incidence) of species in a sample of quadrats or other sampling units, which covers interpolation between zero and the observed number of samples, as well as extrapolation beyond the observed sample set.
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Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages
Robert K. Colwell,Anne Chao,Nicholas J. Gotelli,Shang Yi Lin,Chang Xuan Mao,Robin L. Chazdon,John T. Longino +6 more
TL;DR: In this paper, the authors provide new unconditional variance estimators for classical, individual-based rarefaction and for Coleman Rarefaction under two sampling models: sampling-theoretic predictors for the number of species in a larger sample (multinomial model), a larger area (Poisson model) or a larger number of sampling units (Bernoulli product model), based on an estimate of asymptotic species richness.
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A sequential importance sampling algorithm for generating random graphs with prescribed degrees
TL;DR: An extension of a combinatorial characterization due to Erdős and Gallai is used to develop a sequential algorithm for generating a random labeled graph with a given degree sequence, which allows for surprisingly efficient sequential importance sampling.