تخمین نقطه‌ی تغییر پله‌ای در پروفایل‌های خطی چندگانه با استفاده از شبکه‌ی عصبی احتمالی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی صنایع، دانشکده فنی و مهندسی، واحد تهران غرب، دانشگاه آزاد اسلامی، تهران، ایران

2 استادیار گروه مهندسی صنایع، واحد تهران غرب، دانشگاه آزاد اسلامی، تهران، ایران

3 استادیار گروه مهندسی صنایع، دانشکده فنی و مهندسی، واحد تهران غرب، دانشگاه آزاد اسلامی، تهران، ایران

چکیده

تخمین نقطه‌ی تغییر با کاهش مدت زمان لازم و هزینه‌ی تشخیص عامل اثرگذار، به مهندسین کیفیت در جستجوی مؤثر برای کشف انحرافات با دلیل و بهبود کیفیت یک محصول یا یک فرآیند کمک می‌کند. در این مقاله، از رویکرد شبکه‌ی عصبی مصنوعی احتمالی برای تخمین نقطه‌ی تغییر پله‌ای در فاز 2 پایش پروفایل‌های چندگانه استفاده می‌شود. عملکرد شبکه‌ی عصبی احتمالی پیشنهادی برای تخمین نقطه‌ی تغییر با استفاده از شبیه‌سازی مونت‌کارلو مورد سنجش قرار می‌گیرد. نتایج شبیه‌سازی‌ها بیانگر این است که شبکه‌ی پیشنهادی در تخمین نقطه‌ی تغییر، در تغییرات کوچک از رویکرد حداکثر درستنمایی بهتر عمل می‌کند. اما در تغییرات متوسط تا بزرگ رویکرد حداکثر درستنمایی بهتر عمل می‌کند. به علاوه، مزیت دیگر رویکرد پیشنهادی این است که بر خلاف رویکرد حداکثر درستنمایی نیازی به هیچ گونه پیش آگاهی از نوع تغییر ندارد و به خوبی قادر به تخمین انواع نقاط تغییر نیز هست.

کلیدواژه‌ها


عنوان مقاله [English]

Step Change Point Estimation in the Mean of Multiple Linear Profiles by Probabilistic Neural Network 

نویسندگان [English]

  • Negin Forouzandeh 1
  • Mona Ayoubi 2
  • Masoomeh Zeinalnezhad 3
1   Master Student of Industrial Engineering Department, College of  Engineering, West Tehran Branch, Islamic Azad University,
2 (Corresponding author) Assistant Professor of Industrial Engineering Department, College of Engineering, West Tehran Branch, Islamic Azad University,  
3 Assistant Professor of Industrial Engineering Department, College of  Engineering, West Tehran Branch, Islamic Azad University,  
چکیده [English]

The change point is a useful concept in the control of statistical process that assists quality engineers in finding assignable causes and improving the quality of a product or process. It also reduces the time and cost of detecting assignable causes. In this paper, an artificial neural network approach is used to estimate the step change point in phase II monitoring of the mean of multiple linear profiles. the performance of the probabilistic neural network is evaluated in order to estimate the change point utilizing Monte Carlo Simulation. The results of simulations show the fact that the network recommended in estimating the change point has entirely better performance than maximum likelihood estimator in small shifts, considering mean square error criteria, but maximum likelihood estimator method owns better performance in medium to large shifts. In general, in all shift types, maximum likelihood estimator has a better performance in terms of precision, and the proposed probabilistic neural network performs better in terms of accuracy. In addition, another advantage of the proposed approach is the fact that contrary to the maximum likelihood estimator approach, it does not require any knowledge about the change type and can appropriately estimate any kind of change point as well. 
 

کلیدواژه‌ها [English]

  • Change point estimation
  • Profiles monitoring
  • Multiple linear profiles
  • Artificial neural network
  • Statistical process control (SPC). 
 
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