التنبؤ بزمن الصيانة باستخدام السلاسل الزمنية
Abstract
This research includes a methodology to study the times of machine repairs of the recorded data for the time of Breakdown Time as a time series, and to analyze them in order to arrive at the best predictive model of repair times for later periods. In order to make decisions and measures for the proper planning of maintenance activities, Jenkins In building the four-phase time series model, recognition, estimation, diagnosis and prediction, The R-code package was applied to statistical applications and programmers.
The results showed that the smallest value of Bayesian Information Criterion )BIC( was AR (1) and MA (0). The predicted model is AR (1) and can be written in general form Autoregressive Integrated Moving Average ARIMA (1, 0, 0) for the studied time series.
After checking the stationary of the time series, the machine repair time was predicted for five subsequent periods, and the new time series was plotting.
التنبؤ بزمن الصيانة باستخدام السلاسل الزمنية
يتضمن هذا البحث منهجية لدراسة أزمنة إصلاحات الآلات باستخدام البيانات المسجلة التي تخص زمن إصلاح الأعطال Breakdown Time كسلسلة زمنية، والعمل على تحليلها للوصول إلى أفضل نموذج تنبؤي بأزمنة الإصلاح لفترات لاحقة بغية اتخاذ القرارات والتدابير اللازمة للتخطيط الجيد لأنشطة الصيانة، تم دراسة استقرار السلسلة ومن ثم استخدمت منهجية Box-Jenkins في بناء نموذج السلسلة الزمنية وفق أربعة مراحل هي التعرف والتقدير والتشخيص والتنبؤ، تم تطبيق الحزمة البرمجية R لإجراء التطبيقات والبرامج الإحصائية.
أظهرت النتائج أن أصغر قيمة للمعيارBIC (Bayesian Information Criterion) المحدد لـنموذج ARMA أعطى نموذج التنبؤ AR(1) و MA(0) والذي يمكن كتابته بالشكل العام ARIMA(1, 0, 0) للسلسلة الزمنية المدروسة.
بعد التحقق من استقرار السلسلة، تمت عملية التنبؤ بزمن إصلاح الآلة لفترات لاحقة، ورسم السلسلة الزمنية الجديدة.
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