Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Issue title: Digital transformation through advances in artificial intelligence and machine learning
Guest editors: Hasmat Malik, Gopal Chaudhary and Smriti Srivastava
Article type: Research Article
Authors: Sharma, Sachina; * | Kumar, Vineetb | Rana, K.P.S.b
Affiliations: [a] Department of Instrumentation & Control Engineering, Netaji Subhas Institute of Technology, University of Delhi, Sector 3, Dwarka, New Delhi, India | [b] Department of Instrumentation & Control Engineering, Netaji Subhas University of Technology (Formerly Netaji Subhas Institute of Technology), Sector 3, Dwarka, New Delhi, India
Correspondence: [*] Corresponding author. Sachin Sharma, Department of Instrumentation & Control Engineering, Netaji Subhas Institute of Technology, University of Delhi, Sector 3, Dwarka, New Delhi-110078, India. E-mail: [email protected].
Abstract: Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later.
Keywords: Oscillation detection, Prony method, EMD, IIR filter, deep neural network, cross-correlation, SVM
DOI: 10.3233/JIFS-189778
Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1141-1154, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]