Journal article
Online Steady State Detection Based on Rao‐Blackwellized Sequential Monte Carlo
Quality and reliability engineering international, Vol.32(8), pp.2667-2683
12/2016
DOI: 10.1002/qre.2067
Abstract
Online detection of whether a data stream has reached the steady state is known to be an important problem in many applications such as process control, data reconciliation and fault detection. This paper introduces a novel online steady state detection algorithm under the Bayesian framework based on a multiple change‐point state space formulation and the sequential Monte Carlo methods. A Rao‐Blackwellization technique is proposed to substantially reduce the variance of Monte Carlo estimation and greatly enhance the computational efficiency. In addition, a resampling scheme called the Optimal Resampling is used for eliminating duplicate samples and the robustness of steady state detection is significantly improved by using the information of the particles more efficiently. Numerical studies based on simulated signals and application to a real data set are used to evaluate the performance of the proposed method and compare with other existing methods from the literature. The proposed method is shown to establish a more robust performance than other methods. And it is much more computationally efficient than the standard sequential Monte Carlo method. Copyright © 2016 John Wiley & Sons, Ltd.
Details
- Title: Subtitle
- Online Steady State Detection Based on Rao‐Blackwellized Sequential Monte Carlo
- Creators
- Yuxing Hou - University of IowaJianguo Wu - The University of Texas at El PasoYong Chen - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Quality and reliability engineering international, Vol.32(8), pp.2667-2683
- DOI
- 10.1002/qre.2067
- ISSN
- 0748-8017
- eISSN
- 1099-1638
- Number of pages
- 17
- Grant note
- National Science Foundation (CMMI‐1335454; IIS‐1343974)
- Language
- English
- Date published
- 12/2016
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984186968502771
Metrics
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