Predictive maintenance of an industrial laser using statistical process control charting

Author(s):  
James G. Katter ◽  
Jay F. Tu ◽  
Lawrence E. Monacelli ◽  
Mark Gartner
Author(s):  
Janine A. Purcell

To develop usable Human-Machine Systems, we need Tools to evaluate and measure the length of learning periods, error rate, response time, and transfer of learning in the human operators of these systems (Whiteside, Bennett, and Holtzblatt, 1988). This research explores the use of Statistical Process Control (SPC) charts as a tool to visualize and analyze performance in a decision-making task. The data submitted to control charting was collected in an experiment that explored the effect of order of training or experience in working with alternate display formats. Results for an individual subject as well as a summary for one of the four experimental groups are discussed. Suggestions for further applications of these techniques are offered.


2021 ◽  
Vol 3 (5) ◽  
pp. 3730-3749
Author(s):  
Ana Gessa-Perera ◽  
Eyda Lucía Marín-Ramírez ◽  
María del Pilar Sancha-Dionisio

The purpose of this paper is to propose an approach for incorporating Statistical Process Control (SPC) charting technique to monitor and continuously improve the learning processes by monitoring the satisfaction of students who used an interactive computer-based learning material, which was produced to solve problems in the Operation Management course. By applying SPC methods, the authors examine the sources of process variation (common or special causes) and analyse the ability of teaching strategies to ensure the acquisition and development of the competencies and skills demanded in current university studies. A total of 184 students participated in the learning experience. The findings show that the learning process is under control and therefore the variation of process is due to common causes. However, the Capability Analysis carried out, reveals that process has not enough capacity to achieve the specifications required by the teachers involved in the educational project. This quantitative approach will not only allow self-assessment, but can be used for comparative purposes (other teaching strategies, colleges, etc.). Thus, the control charting is a complementary assessment technique that should be included within the current Quality and Learning Assurance Systems at higher education institutions.


1998 ◽  
Vol 10 (4) ◽  
pp. 161-169 ◽  
Author(s):  
James G. Katter ◽  
Jay F. Tu ◽  
Lawrence E. Monacelli ◽  
Mark Gartner

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 23427-23439 ◽  
Author(s):  
Jyh-Yih Hsu ◽  
Yi-Fu Wang ◽  
Kuan-Cheng Lin ◽  
Mu-Yen Chen ◽  
Jenneille Hwai-Yuan Hsu

2012 ◽  
Vol 39 (6Part13) ◽  
pp. 3750-3750 ◽  
Author(s):  
C Able ◽  
C Hampton ◽  
A Baydush ◽  
M Bright

Sports ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 105 ◽  
Author(s):  
William Sands ◽  
Marco Cardinale ◽  
Jeni McNeal ◽  
Steven Murray ◽  
Christopher Sole ◽  
...  

Athletes who merit the title ‘elite’ are rare and differ both quantitatively and qualitatively from athletes of lower qualifications. Serving and studying elite athletes may demand non-traditional approaches. Research involving elite athletes suffers because of the typical nomothetic requirements for large sample sizes and other statistical assumptions that do not apply to this population. Ideographic research uses single-athlete study designs, trend analyses, and statistical process control. Single-athlete designs seek to measure differences in repeated measurements under prescribed conditions, and trend analyses may permit systematic monitoring and prediction of future outcomes. Statistical process control uses control charting and other methods from management systems to assess and modify training processes in near real-time. These methods bring assessment and process control into the real world of elite athletics.


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