عیب یابی ناشی از سوپاپ‌های کمپرسور رفت و برگشتی با استفاده از شبکه عصبی

نوع مقاله : مقاله پژوهشی

نویسندگان

هادی کلانی ¹

محمد افکن پور ²

ابوطالب قلیزاده ³

احمد حاجی پور ⁴

¹ دانشکده مهندسی مکانیک، دانشگاه سجاد، مشهد، ایران

² شرکت دانش بنیان اطلس پترو پویش پاژ- پارک علم و فناوری خراسان رضوی- مشهد- ایران

³ شرکت دانش بنیان اطلس پترو پویش پاژ- پارک علم و فناوری خراسان رضوی-مشهد-ایران

⁴ گروه مهندسی مکانیک، دانشگاه حکیم سبزواری، سبزوار، ایران.

10.22067/jacsm.2026.96210.1376

چکیده

چکیده: کمپرسورهای رفت و برگشتی از ماشین‌آلات رایج در کاربردهای صنعتی به شمار می‌روند. توقف‌های ناگهانی و فعالیت‌های تعمیر و نگهداری غیرزمان‌بندی شده این کمپرسورها، خسارت قابل توجهی در میزان تولید و بهره‌وری واحد صنعتی به همراه دارد. از میان تمامی خرابی‌هایی که باعث توقف ناگهانی کمپرسورهای رفت و برگشتی می‌شوند، خرابی‌های مرتبط با سوپاپ بیشترین شیوع را دارند. هدف این مقاله، بررسی تشخیص و پیش‌بینی خرابی‌ سوپاپ­ها با استفاده از شبکه عصبی پرسپترون چندلایه، تابع پایه شعاعی و روش گروه داده‌ها بر اساس سیگنال‌های فشار، دما و ارتعاش کمپرسورهای رفت و برگشتی می باشد. این پارامترها به صورت پیوسته اندازه‌گیری شده و مقادیر پایه‌ای برای شرایط عادی و خرابی تعریف شده‌اند. در هر مدل شبکه عصبی، ساختار بهینه با بهره‌گیری از الگوریتم ژنتیک تعیین شده است. از نظر کمی، مدل بهینه‌شده روش MLPANN نسبت به مدل‌های بهینه شده RBFANN و GMDH عملکرد بهتری نشان داد؛ به‌طوری‌که مقدار RMSE حدود ۹۵٪ کاهش و ضریب همبستگی حدود ۰٫۳٪ افزایش یافت.
 

کلیدواژه‌ها

 کمپرسور رفت و برگشتی

عیب یابی

سوپاپ

یادگیری ماشین

موضوعات

• ترمودینامیک

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عنوان مقاله English

Fault Diagnosis of a Reciprocating Compressor Valve Based on Neural Networks

نویسندگان English

Hadi Kalani ¹

Mohammad Afkanpour ²

Abutaleb Gholizadeh ³

Ahmad Hajipour ⁴

¹ Mechanical Engineering Department, Sadjad University, Mashhad, Iran.

² Atlas Petro Poyuseh Paj, Knowledge Based Company, Khorasan Science and Technology Park, Mashhad, Iran

³ Atlas Petro Poyuseh Paj, Knowledge Based Company, Khorasan Science and Technology Park, Mashhad, Iran

⁴ Mechanical Engineering Department, Hakim Sabzevari University, Sabzevar, Iran.

چکیده English

Reciprocating compressors are commonly used machinery for industrial applications. Unscheduled downtime and maintenance activity on the compressors causes considerable loss in throughput and efficiency of a plant. Of all the failures that cause unscheduled downtime in reciprocating compressors, valve related causes are predominant. The objective of this paper is to research detection and prediction of valve failures by Multi-layer perceptron neural network, MLPANN, Radial basis function, RBFANN, and Group Method of Data Handling, GMDH analysis of pressure, temperature and vibration signals, which are the most common measurements on a reciprocating compressor system. These parameters are measured on a continuous basis and baselines are established for normal (or acceptable) behavior and failure (or fault) condition. In each model, the optimized structure is obtained through a Genetic algorithm, GA, technique. The results indicated no statistically significant differences among the three models. However, the MLPANN neural network demonstrated superior performance in fault diagnosis.

کلیدواژه‌ها English

Reciprocating compressors

Fault detection

Vlave

Machine learning

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