Industrial time series forecasting based on improved Gaussian process regression
- PDF / 1,773,914 Bytes
- 17 Pages / 595.276 x 790.866 pts Page_size
- 93 Downloads / 270 Views
METHODOLOGIES AND APPLICATION
Industrial time series forecasting based on improved Gaussian process regression Tianhong Liu1,2 · Haikun Wei3 · Sixing Liu4 · Kanjian Zhang3
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Industrial processes often include shifting operating phases and dynamics, and system uncertainty. Industrial time series data may obey different distributions because of the time-varying characteristic. Therefore, a single global model cannot describe the local characteristics of multiple distributions. In this work, a hybrid GMM-IGPR model is proposed to solve this kind of time series prediction problem by using an improved Gaussian process regression (GPR) based on Gaussian mixture model (GMM) and a variant of the basic particles swarm optimization (PSO). In a first treatment to the time series, different distributions of the original dataset are characterized by adopting the GMM as a cluster method. Then, multiple localized GPR models are built to characterize the different properties between inputs and output within various clusters. In order to optimize the proposed algorithms, this paper utilizes the DEPSO which introduces differential evolution (DE) operator into the basic PSO algorithm to estimate hyperparameters of the GPR model, instead of using the traditional conjugate gradient (CG) method. Lastly, the Bayesian inference strategy is used to estimate the posterior probabilities of the test data with respect to different clusters. The various localized GPR models are integrated through these posterior probabilities as the weightings so that a global predictive model is developed for the final prediction. The effectiveness of the proposed algorithm is verified by means of a numerical example and a real industrial winding process. Statistical tests of experimental results compared with other popular prediction models demonstrate the good performance of the proposed model. Keywords Predictive modeling · Industrial time series · Gaussian process regression · Gaussian mixture model · Particle swarm optimization
1 Introduction Industrial data generally refer to a large amount of diversified time series generated at a high speed by automated industrial equipments and processes (General Electric Intelligent Communicated by V. Loia.
B
Tianhong Liu [email protected]
1
School of Information Engineering, Yangzhou University, Yangzhou 225127, People’s Republic of China
2
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People’s Republic of China
3
Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, People’s Republic of China
4
School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, People’s Republic of China
Platforms 2012). With the availability of large amounts of data, industries are increasingly looking toward new ways for extracting useful information. An accurate prediction of key variables of industrial
Data Loading...
