توسعه مدل پیش‌بینی خرابی کمپرسورهای پتروشیمی مبتنی بر داده‌کاوی: مطالعه موردی مجتمع پتروشیمی مهاباد

چاوشینی, فرهاد; یوسفی نژاد عطاری, مهدی; لطفیان دلویی, فهیمه

doi:10.66224/jii.3.3.39

توسعه مدل پیش‌بینی خرابی کمپرسورهای پتروشیمی مبتنی بر داده‌کاوی: مطالعه موردی مجتمع پتروشیمی مهاباد

نوع مقاله : مقاله پژوهشی

نویسندگان

فرهاد چاوشینی ¹

مهدی یوسفی نژاد عطاری ¹

فهیمه لطفیان دلویی ²

¹ گروه مهندسی صنایع، واحد بناب، دانشگاه آزاد اسلامی، بناب، ایران.

² گروه مهندسی مکانیک، دانشکده فنی و مهندسی، دانشگاه زابل، زابل، ایران.

10.66224/jii.3.3.39

چکیده

تجهیزات مورد استفاده در فرآیند تولید، در طول چرخه عمر عملیاتی خود نیازمند نگهداری و تعمیرات هستند. توقف خطوط تولید، اغلب هزینه‌های بالایی دارد. یافتن راهکارهایی برای اقدامات پیش‌گیرانه، اهمیت بسزایی دارد. از سوی دیگر، استفاده از تجهیزات پیشرفته با قطعات یدکی گران‌قیمت، و دارای محدودیت‌های سیاسی یا فنی برای خرید، جایگاه سیستم نگهداری و تعمیرات را در سازمان ارتقاء داده و آن را به یکی از مسائل کلیدی در حوزه بهره‌وری تبدیل کرده است. سامانه نگهداری و تعمیرات، نه‌تنها در کاهش هزینه نهایی تولید نقش دارد، بلکه بر کل زنجیره ارزش سازمان نیز اثرگذار است. عواملی مانند سرعت تحویل محصول به مشتری، کیفیت محصول، قابلیت اطمینان تولید، تحت‌تأثیر تصمیمات مرتبط با نگهداری و تعمیرات قرار دارند. در صنایعی مانند پتروشیمی که از ماشین‌آلات پیشرفته در سیستم تولید استفاده می‌شود، بر اهمیت قابلیت اطمینان تجهیزات افزوده می‌شود. در این پژوهش، به‌منظور مطالعه و پیش‌بینی خرابی تجهیزات، از روش‌های مبتنی بر داده‌کاوی شامل ۱304640 داده در بازه زمانی 20 دسامبر ۲۰۲۱ تا ۲۰ می ۲۰۲۲ استفاده شده که توسط ۱۷ حسگر اندازه‌گیری مربوط به یکی از کمپرسورهای ۳۰۰۰ پتروشیمی مهاباد ثبت شده‌اند. این داده‌ها با استفاده از نرم‌افزار Weka مورد تحلیل و داده‌کاوی قرار گرفته‌اند. در ابتدا، توصیف‌های آماری و روابط همبستگی میان داده‌ها تحلیل شده است. سپس با استفاده از روش‌های خوشه‌بندی K-Means، درخت تصمیم، و انتخاب ویژگی‌ها، علل خرابی تجهیزات و مسیرهای بحرانی منجر به توقف کمپرسور مورد ارزیابی قرار گرفته است. در پایان، با توجه به نتایج ، پیشنهاداتی جهت نگهداری و تعمیرات حسگرهای بحرانی ارائه شده است.

کلیدواژه‌ها

داده‌کاوی

صنعت پتروشیمی

خوشه‌بندی

درخت تصمیم

مسیر بحرانی

عنوان مقاله English

Development of a Data Mining-Based Predictive Model for Petrochemical Compressor Failures: A Case Study of Mahabad Petrochemical Complex

نویسندگان English

Farhad Chavoshini ¹

Mahdi Yousefinejad Attari ¹

Fahime Lotfian Delouyi ²

¹ Department of Industrial Engineering, Bon.C., Islamic Azad University, Bonab, Iran.

² Department of Mechanical Engineering, Facuity of Engineering, Zabol University, Zabol, Iran.

چکیده English

A majority of production equipment worldwide today experiences failures in its working life cycle and must be repaired and maintained. Since production downtime incurs enormous financial implications, it is extremely crucial to find solutions for taking preventive action. Moreover, the use of sophisticated equipment—whose spare parts are expensive and sometimes unavailable due to political or technical limitations—has elevated the status of maintenance systems within companies, with it becoming a key determinant in productivity. A maintenance system not only reduces the final cost of production but also impacts the overall organizational value chain. Elements such as production speed and delivery time, product quality, production line reliability, and organizational flexibility all come under the influence of maintenance decisions. Therefore, in industries such as petrochemicals, where high-level machinery is utilized in the production systems, equipment reliability and maintenance gain greater significance. In this study, to analyze and predict equipment failures by using data mining methods, a 1304640 record dataset measured between December 20, 2021, and May 20, 2022, from 17 measurement sensors on one of the 3000 -series compressors of the Mahabad Petrochemical Complex were used. These data were examined, analyzed, and mined with Weka software. The statistical summaries and correlation relationships among the data were studied initially. Then, the reasons for equipment failure and the critical path leading to compressor shutdown were evaluated using the application of K-Means clustering, decision trees, and feature selection methods. Finally, based on the findings, recommendations were provided to focus maintenance efforts on critical sensors.

کلیدواژه‌ها English

Data Mining

Petrochemical Industry

Clustering

Decision Tree

Critical Root Cause

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