scholarly journals Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques

2020 ◽  
Author(s):  
Mohammed Ziyan Sheriff ◽  
Nour Basha ◽  
Muhammad Nazmul Karim ◽  
Hazem Nounou ◽  
Mohamed Nounou
Keyword(s):  
Fault Detection ◽  
Process Monitoring ◽  
Statistical Process ◽  
Monitoring Techniques ◽  
Statistical Process Monitoring ◽  
Interval Valued

scholarly journals Statistical Process Monitoring with Biogeography-Based Optimization Independent Component Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xiangshun Li ◽  
Di Wei ◽  
Cheng Lei ◽  
Zhiang Li ◽  
Wenlin Wang
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Process Monitoring ◽  
Iteration Method ◽  
Newton Iteration ◽  
Independent Component ◽  
Statistical Process ◽  
Independent Components ◽  
Newton Iteration Method ◽  
Statistical Process Monitoring

Independent Component Analysis (ICA), a type of typical data-driven fault detection techniques, has been widely applied for monitoring industrial processes. FastICA is a classical algorithm of ICA, which extracts independent components by using the Newton iteration method. However, the choice of the initial iterative point of Newton iteration method is difficult; sometimes, selection of different initial iterative points tends to show completely different effects for fault detection. So far, there is still no good strategy to get an ideal initial iterative point for ICA. To solve this problem, a modified ICA algorithm based on biogeography-based optimization (BBO) called BBO-ICA is proposed for the purpose of multivariate statistical process monitoring. The Newton iteration method is replaced with BBO here for extracting independent components. BBO is a novel and effective optimization method to search extremes or maximums. Comparing with the traditional intelligent optimization algorithm of particle swarm optimization (PSO) and so on, BBO behaves with stronger capability and accuracy of searching for solution space. Moreover, numerical simulations are finished with the platform of DAMADICS. Results demonstrate the practicability and effectiveness of BBO-ICA. The proposed BBO-ICA shows better performance of process monitoring than FastICA and PSO-ICA for DAMADICS.

Integrating statistical process monitoring with feedforward control

2000 ◽  
Vol 16 (6) ◽  
pp. 515-525 ◽  
Author(s):  
Douglas C. Montgomery ◽  
J. B. Keats ◽  
Mark Yatskievitch ◽  
William S. Messina
Keyword(s):  
Process Monitoring ◽  
Feedforward Control ◽  
Statistical Process ◽  
Statistical Process Monitoring

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Angelo Marcio Oliveira Sant’Anna
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Particle Swarm ◽  
Injection Rate ◽  
Statistical Process ◽  
Swarm Optimization ◽  
Content Type ◽  
Statistical Process Monitoring

PurposeE-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.Design/methodology/approachAn approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.FindingsThe results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.Originality/valueThis research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

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