Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture

Shakir Khan; Tamanna Siddiqui; Azrour Mourade; Bayan Ibrahimm Alabduallah; Saad Abdullah Alajlan; Abrar Almjally; Bader M Albahlal; Amani Alfaifi

doi:10.1007/s00170-023-11602-y

Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture

Int J Adv Manuf Technol. 2023 Jun 6:1-13. doi: 10.1007/s00170-023-11602-y. Online ahead of print.

Authors

Shakir Khan^{1

2}, Tamanna Siddiqui³, Azrour Mourade⁴, Bayan Ibrahimm Alabduallah⁵, Saad Abdullah Alajlan¹, Abrar Almjally¹, Bader M Albahlal¹, Amani Alfaifi¹

Affiliations

¹ College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
² Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Mohali, 140413 India.
³ Department of Computer Science, Aligarh Muslim University, Aligarh, UP India.
⁴ Computer Sciences Department, Faculty of Sciences and Technics, Moulay Ismail University, Meknes, Morocco.
⁵ Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671 Saudi Arabia.

Abstract

Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.

Keywords: Automation; Classification; Deep learning; FL_SDDAE; LSEBPNN; Soft sensors.

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