Journal ArticleDOI
GNAR-GARCH model and its application in feature extraction for rolling bearing fault diagnosis
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TLDR
A hybrid model, the combination of a general expression for linear and nonlinear autore progressive (GNAR) model and a generalized autoregressive conditional heteroscedasticity (GARCH) model is proposed and applied to rolling bearing fault diagnosis, showing higher accuracy and better performance than do other models.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2017-09-01. It has received 41 citations till now. The article focuses on the topics: Akaike information criterion & Autoregressive model.read more
Citations
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Journal ArticleDOI
Applications of machine learning to machine fault diagnosis: A review and roadmap
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
Journal ArticleDOI
Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification
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Research on fault diagnosis methods for the reactor coolant system of nuclear power plant based on D-S evidence theory
TL;DR: A new fault diagnosis method for the main coolant system of nuclear power plant based on D-S evidence theory is presented and demonstrates that the proposed model can not only get more accurate judgments under some cases, but also provide flexibility in dealing with uncertain information.
Journal ArticleDOI
An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis.
TL;DR: Comparisons illustrate the superiority of SP over kurtosis for selecting the sensitive mode from the resulted signal of CCEEMEDAN and over two other popular signal-processing methods, variational mode decomposition and fast kurtogram.
Journal ArticleDOI
Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis
TL;DR: A joint distribution adaptation network with adversarial learning is developed to effectively tackle cross-domain fault diagnosis issues and can achieve precisely distribution matching, and extract the category-discriminative and domain-invariant features between the source and target domains.
References
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Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Journal ArticleDOI
Another look at measures of forecast accuracy
Rob J. Hyndman,Anne B. Koehler +1 more
TL;DR: In this paper, the mean absolute scaled error (MESEME) was proposed as the standard measure for comparing forecast accuracy across multiple time series across different time series types, and was used in the M-competition as well as the M3competition.