4 research outputs found
An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analyzers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach. Besides, the weighted mean of the monitoring statistics aforementioned is utilized as a metrics to detect potential abnormalities. The virtues of the proposed algorithm are discussed in comparison with several unsupervised algorithms. Finally, Tennessee Eastman process and an autosuspension model are employed to demonstrate the effectiveness of the proposed scheme further
AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes
Smart Manufacturing refers to optimization techniques that are implemented in
production operations by utilizing advanced analytics approaches. With the
widespread increase in deploying Industrial Internet of Things (IIoT) sensors
in manufacturing processes, there is a progressive need for optimal and
effective approaches to data management. Embracing Machine Learning and
Artificial Intelligence to take advantage of manufacturing data can lead to
efficient and intelligent automation. In this paper, we conduct a comprehensive
analysis based on Evolutionary Computing and Deep Learning algorithms toward
making semiconductor manufacturing smart. We propose a dynamic algorithm for
gaining useful insights about semiconductor manufacturing processes and to
address various challenges. We elaborate on the utilization of a Genetic
Algorithm and Neural Network to propose an intelligent feature selection
algorithm. Our objective is to provide an advanced solution for controlling
manufacturing processes and to gain perspective on various dimensions that
enable manufacturers to access effective predictive technologies.Comment: 13 pages, 7 figures. To appear in IEEE/CAA JA