Kalman Filtering for Manufacturing Processes

Oakes, Thomas; Tang, Lie; Landers, Robert G.; Balakrishnan, S. N.

doi:10.5772/6819

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…These territorial disputes cause conflict and complicate cooperative management of transboundary populations (Dharmadi et al., 2015; Zhang, 2018). China is a main importer and consumer of shark fins (Dent & Clarke, 2015; Oakes & Sant, 2019), but their reports to the FAO do not provide true volumes or locations of catch (Dent & Clarke, 2015; FishStatJ). Targeted shark fisheries in southern China collapsed between the 1970s and 1990s (Lam & de Mitcheson, 2011), and reconstructed elasmobranch catches suggest a decline of 67% since the 1950s (Zeller & Pauly, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…Southeast Asia contained 3 of the top 20 elasmobranch fishing nations from 2000 to 2011 (Indonesia, Malaysia, and Thailand [Dent & Clarke, 2015]) and 2 of the top 20 elasmobranch fishing nations from 2007 to 2017 (Indonesia and Malaysia) (Oakes & Sant, 2019). Total landings of elasmobranchs reported to the Food and Agriculture Organization (FAO) (Figure 1a) are likely 3–4 times lower than actual catches (Clarke et al., 2006; Worm et al., 2013); however, reconstructed data (Sea Around Us, 2021) can be used to make estimates (Figure 1b).…”

Section: Resultsmentioning

confidence: 99%

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Status of Southeast Asia's marine sharks and rays

Clark‐Shen

Chin

Arunrugstichai³

et al. 2022

Conservation Biology

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Status of Southeast Asia's marine sharks and rays

Clark‐Shen

Chin

Arunrugstichai³

et al. 2022

Conservation Biology

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“…satisfying x ∈ R n . A detailed study on the application of DA in discrete manufacturing can be found in [75][76][77]. In the next section, we will derive the specific implementation algorithms for each block in the architecture.…”

Section: Da Architecture For Dtmentioning

confidence: 99%

A generalized data assimilation architecture of digital twin for complex process industrial systems

Zhao,

Jiang,

Cai

et al. 2024

Meas. Sci. Technol.

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As one of the critical cores of digital twin (DT), data assimilation (DA) can maintain consistency and synchronization between DT and physical system. Kalman filtering is a common DA method, but its estimation performance is deteriorated by factors such as model inaccuracy and time-varying noise covariance in practical applications. The errors caused by these multiple uncertainties are all coupled in the measurements, which augments the difficulty for DT to obtain physical system information. In order to tackle the DA problem with multiple uncertainties, this paper proposes a generalized DA architecture of DT for sophisticated process industry. First, combining Stein variational gradient descent (SVGD) and nonlinear Bayesian filtering paradigm, a recursive estimation framework is established, which has higher accuracy in estimating the noise covariance compared to traditional methods. Second, to effectively deal with model inaccuracy by using the filtering residual containing time-varying noise, we propose a neural network and modified wavelet-based model error compensation (NNMW-MEC) block. Based on modified wavelet technique, the filtering residual denoising (FRD) built in NNMW-MEC can better cope with time-varying noise compared to existing wavelets, and extract the low-frequency signal involving model error information from noisy residual smoothly. In addition, because of the neural network-based state-compensation (NNSC) subblock, NNMW-MEC has more outstanding ability in compensating the state deviations with large changing range. Finally, we take the boiler system in a coal-fired power plant as an example to verify the effectiveness of our architecture. Experimental results show that the DA architecture proposed in this paper can improve the estimation performance of DT under inaccurate model and uncertain noise statistics.

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“…Kalman filter is used in supply chain as abstraction [27]. It is also useful in manufacturing process to improve the capability of overall process [28]. Another useful application of Kalman filter was reported in literature to estimate parameters of train i.e.…”

Section: Introductionmentioning

confidence: 99%

Kalman filter based short term prediction model for COVID-19 spread

et al. 2020

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Corona Virus Disease 2019 (COVID19) has emerged as a global medical emergency in the contemporary time. The spread scenario of this pandemic has shown many variations. Keeping all this in mind, this article is written after various studies and analysis on the latest data on COVID19 spread, which also includes the demographic and environmental factors. After gathering data from various resources, all data is integrated and passed into different Machine Learning Models in order to check its appropriateness. Ensemble Learning Technique, Random Forest, gives a good evaluation score on the tested data. Through this technique, various important factors are recognized and their contribution to the spread is analyzed. Also, linear relationships between various features are plotted through the heat map of Pearson Correlation matrix. Finally, Kalman Filter is used to estimate future spread of SARS-Cov-2, which shows good results on the tested data. The inferences from the Random Forest feature importance and Pearson Correlation gives many similarities and few dissimilarities, and these techniques successfully identify the different contributing factors. The Kalman Filter gives a satisfying result for short term estimation, but not so good performance for long term forecasting. Overall, the analysis, plots, inferences and forecast are satisfying and can help a lot in fighting the spread of the virus.

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Kalman Filtering for Manufacturing Processes

Cited by 6 publications

References 5 publications

Status of Southeast Asia's marine sharks and rays

Status of Southeast Asia's marine sharks and rays

A generalized data assimilation architecture of digital twin for complex process industrial systems

Kalman filter based short term prediction model for COVID-19 spread

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