Machine Learning Architecture in Soft Sensor for Manufacturing Control and Monitoring System Based on Data Classification

Main Article Content

Dr. N. Shanthi
Shreyas J

Abstract

Deep learning, a feature representation method that was just recently developed for data with complex structures, has a lot of potential for soft sensing of industrial processes. However, with the unprocessed observed input data, most deep networks primarily concentrate on hierarchical feature learning.This research propose novel technique in soft sensor for manufacturing industry based on controlling and monitoring using machine learning techniques. Here the data has been collected as IoT based monitored data and processed for noise removal, normalization. The processed data classified for detection of faults using probalistic convolutional neural network. the control system is carried out using weighted auto-encoderbelief network (WAEBN). The experimental analysis has been carried out in terms of QoS, measurement accuracy, RMSE, MAE, prediction performance.

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How to Cite
Shanthi, D. N. ., & J, S. . (2022). Machine Learning Architecture in Soft Sensor for Manufacturing Control and Monitoring System Based on Data Classification. Research Journal of Computer Systems and Engineering, 2(2), 01:05. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/24
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