Multivariate Time-Series Modeling for Forecasting Sintering Temperature in Rotary Kilns Using DCGNet

Zhang, Xiaogang; Lei, Yanying; Chen, Hua; Zhang, Lei; Zhou, Yicong

doi:10.1109/tii.2020.3022019

Cited by 32 publications

(11 citation statements)

References 28 publications

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“…The historical and current time sequences were respectively transformed into the historical and current trend sequences by equations ( 18) and (19). Then, the sub-sequences in the historical trend sequences, being the same as the sub-sequence of recent trends in (20), were used for prediction. In addition, a posterior probability and its lower bound were calculated from the Bayesian estimators.…”

Section: Discussionmentioning

confidence: 99%

“…Yang et al combined different tree-based machine learning algorithms into an enhanced machine learning model and predicted bubble point pressure based on composition data and temperature [19] . Zhang et al used convolution neural networks to extract the patterns from historical data and designed a deep neural network to incorporate the pattern characteristics for forecasting the sintering temperature [20] . Bogojeski et aldetermined the sample size used for training models on a synthetic data set with known dynamics and predicted industrial ageing processes based on the trained recurrent models [21] .…”

Section: Introductionmentioning

confidence: 99%

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Alarm state forecasting from similar trends in historical time sequences based on Bayesian estimators

Kang

et al. 2023

Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023)

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Predicting the upcoming alarm state helps industrial plant operators take timely actions to avoid the negative effects of abnormal conditions. This paper proposes a new data-driven method for trend forecasting in a process variable. The proposed method has two important features: (i) alarm states are predicted in real-time whenever a new trend occurs in the current time sequence, which helps industrial operators take timely actions at the early stage of abnormal conditions; (ii) alarm states are predicted with quantitative probabilities and credibility intervals. By contrast, existing early warning methods are either suitable for specific scenarios where a short prediction step length is needed or cannot provide a quantitative measurement of prediction results. The proposed method firstly captures qualitative trends in the current and historical time sequences and clusters these trends into several categories. Secondly, current and historical trend sequences are obtained according to the cluster results. Finally, recent trends in the current trend sequence are chosen, and alarm states are predicted based on the historical trend sequence having the same sub-sequence. A Bayesian estimator is exploited to calculate the posterior probabilities and their credibility intervals for predicted alarm states. The optimal predicted alarm state is the one with the largest lower bound of posterior probability as a quantitative measurement of prediction accuracy. The effectiveness of the proposed method is illustrated via numerical examples based on a well-accepted benchmark of the Tennessee Eastman process.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alarm state forecasting from similar trends in historical time sequences based on Bayesian estimators

Kang

et al. 2023

Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023)

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show abstract

“…σ 2 1 and σ 2 2 are variances of v 1 (k) and v 2 (k). Taking the noise variances σ 2 1 = σ 2 2 = 0.20 2 , utilizing the M-GESG algorithm and the F-M-PC-GESG algorithm to identify system parameters, parameter estimates and their errors δ := θ(k) − ϑ / ϑ are given in Table 1. The parameter identification errors versus k are given in Figure 2.…”

Section: Simulationsmentioning

confidence: 99%

“…Consider another multivariate equation-error autoregressive moving average system, The configuration of the simulation in this example is the same as in Example 1. Set noise variances σ 2 1 = 0.50 2 and σ 2 2 = 0.40 2 , and utilize the M-GESG algorithm and the F-M-PC-GESG algorithm to identify the system parameters. The parameter estimates and their errors are given in Table 2.…”

Section: Simulationsmentioning

confidence: 99%

“…The foundation of industrial automatic production and intelligent control is a precise model of the production processes [1]. With the expansion of production scale, multivariate systems have been widely used in production processes [2][3][4]. Parameter estimation for multivariate systems has taken a considerable role in system identification and has attracted much attention from researchers in recent decades.…”

Section: Introductionmentioning

confidence: 99%

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A Filtering-Based Stochastic Gradient Estimation Method for Multivariate Pseudo-Linear Systems Using the Partial Coupling Concept

Ma,

Liu,

Chen

2023

Processes

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Solutions for enhancing parameter identification effects for multivariate equation-error systems in random interference and parameter coupling conditions are considered in this paper. For the purpose of avoiding the impact of colored noises on parameter identification precision, an appropriate filter is utilized to process the autoregressive moving average noise. Then, the filtered system is transformed into a number of sub-identification models based on system output dimensions. Founded on negative gradient search, a new multivariate filtering algorithm employing a partial coupling approach is proposed, and a conventional gradient algorithm is derived for comparison. Parameter identification for multivariate equation-error systems has a high estimation accuracy and an efficient calculation speed with the application of the partial coupling approach and the data filtering method. Two simulations are performed to reveal the proposed method’s effectiveness.

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A tensor-based deep LSTM forecasting model capturing the intrinsic connection in multivariate time series

Zhang

Liu

2022

Appl Intell

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Multivariate Time-Series Modeling for Forecasting Sintering Temperature in Rotary Kilns Using DCGNet

Cited by 32 publications

References 28 publications

Alarm state forecasting from similar trends in historical time sequences based on Bayesian estimators

Alarm state forecasting from similar trends in historical time sequences based on Bayesian estimators

A Filtering-Based Stochastic Gradient Estimation Method for Multivariate Pseudo-Linear Systems Using the Partial Coupling Concept

A tensor-based deep LSTM forecasting model capturing the intrinsic connection in multivariate time series

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