بررسی شاخص نرخ مخاطره بر مبنای متغیرهای کمکی مشهود و نامشهود مطالعه موردی معدن جاجرم بوکسیت

نوع مقاله : مقاله مهندسی معدن

نویسندگان

1 دانشجوی دکتری مهندسی استخراج معدن، دانشگاه صنعتی شاهرود

2 دانشکده مهندسی معدن دانشگاه صنعتی شاهرود

3 عضو هیئت‌علمی گروه معدن ، دانشگاه صنعتی شاهرود

4 دکتری مهندسی استخراج معدن، دانشگاه صنعتی شاهرود

چکیده

در صنعت معدنکاری با محیط خشن که بخش‌های مختلف وظیفه کنترل سیستم را عهده‌دار می‌باشند. اطلاعات خرابی‌ها از بخش‌های مختلف، در قالب‌های گوناگون با زمان‌های مختلف و شرایط عملیاتی متفاوت جمع‌آوری می‌شوند. این مسائل ناهمگونی در اطلاعات گردآوری‌شده و بانک داده را به دنبال خواهد داشت. بخشی از ناهمگنی به کمک همین متغیر کمکی مشهود از محاسبات حذف می‌شوند ولی مشکل از جایی نشات می‌یابد که واردکردن تمامی شرایط محیطی امکان‌پذیر نیست. این دسته از عوامل محیطی که منجر به ناهمگنی در داده‌ها می‌شوند به‌عنوان متغیر کمکی نامشهود دسته‌بندی می‌شوند. به همین منظور در این مقاله رویکردی منسجم برای تحلیل عملکرد سیستم بر اساس شاخص قابلیت اطمینان با تأثیر شرایط محیطی پیشنهاد شد. شرایط محیطی در دودسته مشهود که دربرگیرنده عواملی نظیر وضعیت جاده، آب‌وهوا، نوع سنگ،... بوده و نامشهود که دربرگیرنده ناهمگنی است، در نظر گرفته‌شده است. رویکرد پیشنهادی با داده‌هایی از ناوگان باربری معدن بوکسیت جاجرم متشکل از 5 کامیون مورد ارزیابی قرار گرفت. داده‌های موردنیاز از بانک داده ۱۵ ماهه از منابع مختلف چون گزارش‌های روزانه، تعمیرگاه، گزارش‌های هواشناسی، ملاقات و مشاهده‌های مستقیم و مرتب‌شده بر اساس تاریخ وقوع در غالب زمان بین خرابی‌ها و متغیر کمکی استخراج گردید. متغیرهای کمکی بر اساس الگوریتم ارائه‌شده و نتایج حاصله از آزمون‌های تناسب و همگنی مدل نرخ مخاطرات متناسب ترکیبی برای تجزیه‌وتحلیل داده‌های خرابی مورداستفاده قرار گرفت. نتایج به‌دست‌آمده بیانگر تأثیر متغیرهای کمکی نوبت‌کاری، تعداد سرویس‌ها، نوع خردایش و شیب جاده بر روی قابلیت اطمینان کامیون‌ها است.

کلیدواژه‌ها


عنوان مقاله [English]

Observed and Un-Observed Covariate Effects on Baseline Hazard Rate - Case study: Jajarm Bauxite Mine

نویسندگان [English]

  • Reza Barabadi 1
  • Mohammad Ataei 2
  • Reza Khalokakaie 3
  • Ali Nouri Qarahasanlou 4
1 Ph.D. Student of Shahrood University of Technology, Shahrood, Iran
2 Professor, Faculty of Mining, Petroleum & Geophysics, Shahrood University of Technology, Shahrood, Iran
3 Professor, Faculty of Mining, Petroleum & Geophysics, Shahrood University of Technology, Shahrood, Iran
4 PhD. of Mining Engineering, Shahrood University of Technology, Shahrood, Iran
چکیده [English]

In the mining industry, different departments undertake the control task of a system. The failure data from different sections in a variety of formats are collected in different times and operation conditions and these heterogeneity problems will result in the collected data and the data bank. While the studies that are done so far, two items of time and environmental effects have been examined and in fact, the data are assumed homogeneously. However, a part of heterogeneity by the help of observed covariate is omitted, but the problem starts where entering all environmental conditions is not possible. Since some factors such as the effect of management decisions, different brands, operator skills cannot be recorded or they cannot be quantifiable; and in spite of being observable, some are not recorded or not classified. These categories of environmental factors that lead to heterogeneity in the data are categorized as the unobserved covariate. For this purpose, in this paper, a coherent approach was proposed for system performance analysis based on the reliability index with the impact of environmental conditions. Environmental conditions are considered in two observed categories, which include factors such as road conditions, weather conditions, rock types, and unobserved ones that include heterogeneity. The proposed approach was evaluated with data from the mining fleet of Jajarm Bauxite mine consisting of 5 trucks. The significant difference between the results of the inclusion of heterogeneity with its lack of consideration indicates the importance of the impact of unobserved covariates on determining the system's behavior.

کلیدواژه‌ها [English]

  • Mining
  • Reliability
  • Unobserved Covariate
  • Mix proportional hazard model
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