Improving the predictive response using ensemble empirical mode decomposition based soft sensors with auto encoder deep neural network. (August 2022)

• Record Type: Journal Article
• Authors: Lebbe Abdul Haleem, Sulaima; Sodagudi, Suhasini; Althubiti, Sara A; Kumar Shukla, Surendra; Altaf Ahmed, Mohammed; Chokkalingam, Bharatiraja
• Is Part Of: Measurement. Volume 199(2022)
• Publication Date: 2022-08
• Subjects: Soft sensors -- Decomposition -- Neural network -- Signal prediction -- Denoising -- Standardization; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111308
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Signal denoising in signal processing is a fundamental study which is of most essential. The dynamic soft sensor model based on Elman neural network falls under this category. The Ensemble Empirical Mode Decomposition (EEMD) model decomposes the actual input dataset. The prediction results are combined thereby obtaining the final result. Abstract: In industries, several key quality variables used in complex processes are immeasurable online and are not reliable because of the factors like complex environmental criteria, limited techniques for testing and high cost. Soft sensor technology has become known to solve these complexities. In industrial process, the key factors like redundancy, noise and dynamic features of data affect the accuracy of soft sensors. Thus, a predictive control approach is required which has to integrate improved methods used to detect the control signal that uses direction of structural motion. This paper proposes an innovative Ensemble Empirical Mode Decomposition Based Auto Encoder Deep Neural Network (EEMD-AEDNN) which combines the advantages of Ensemble Empirical Mode Decomposition and neural network bringing problems of mode-mixing from EEMD and false modes from neural network under control. Moreover, dynamic characteristics are captured which are distributed over time improving the modelling effects. The advantage is that noise and redundancy from actual data are removed and information loss is minimized. Furthermore, data are sequential introducing historical data for dynamic modelling. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Ensemble Empirical Mode Decomposition based Long Short-Term Memory neural network (EEMD-LSTM), Wavelet Neural Network with Random Time (WNNRT) and Ensemble Empirical Mode Decomposition-General Regression Neural Network (EEMD-GRNN). It is found that the proposed EEMD-AEDNN method achieves 94.22% of accuracy, 84.68% of RMSE, 75.34% of RAE and 54.42% of MAE in 86.5 ms.