ارائه مدل پیش‌بینی ریسک‌های بحرانی شبکه انتقال گاز با استفاده از الگوریتم‌های داده‌کاوی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت صنعتی، ‌گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

2 دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

3 استاد، گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران.

4 دانشیار، گروه مدیریت، دانشکده مدیریت، دانشگاه تهران، تهران، ایران.

چکیده

باتوجه به نقش مهم رویکردهای پیش­بینانه در کاهش هزینه‌­های نگهداری تعمیرات، هدف از انجام پژوهش، ارائه مدل پیش­بینی ریسک­‌های بحرانی و اولویت­‌دار بر پایه الگوریتم­‌های داده­‌کاوی است. روش داده­‌کاوی پژوهش بر اساس روش CRISP طرح‌­ریزی شده است. مدل‌سازی داده­‌ها بر پایه داده‌­کاوی «توصیفی» و«پیش­بینی» و استفاده از الگوریتم­‌های خوشه‌­بندی و طبقه­‌بندی است. شاخص سیلوئیت مبنای خوشه‌­بندی در نظر گرفته شده و از الگوریتم‌­های Two Step، Kohnen و K-Means استفاده شده است. بهترین مقدار، مبتنی بر الگوریتم K-Means برابر 6446/0 با تعداد خوشه 5 بود و ویژگی­‌های اصلی برای انجام طبقه­‌بندی و پیش­بینی ریسک­‌ها تعیین شد. الگوریتم­‌های شبکه عصبی، درخت C.5، نزدیک‌ترین همسایگی و بردار پشتیبان برای طبقه‌­بندی استفاده شده است. در این پژوهش، الگوریتم­ ترکیبی پیش­بینی به‌صورت تکاملی به‌کارگیری شده و در هر مرحله، هدف تقویت میزان صحت و اعتبار مدل طبقه­‌بندی و افزایش یادگیری داده­‌ها است. نتایج پژوهش، یادگیری در56/97 درصد از داده­‌های موردتوافق را نشان داده و میزان صحت و اعتبار مدل ترکیبی برای طبقه­‌بندی داده‌­ها، 86/92 درصد برآورد شده است. بر اساس نتایج، 13 ریسک، بحرانی تشخیص داده شده‌­اند که در این میان «انتشار گازهای آلاینده و مواد شیمیایی» و «عدم‌­آموزش و توجیه­‌نبودن پیمانکاران نسبت به موقعیت شبکه» به‌­ترتیب بیشترین و کمترین اولویت را دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prediction Model of the Gas Pipeline Critical Risk Using Data Mining Algorithms

نویسندگان [English]

  • Mohammad Sadegh Behrouz 1
  • Mohammad Ali AfsharKazemi 2
  • Adel Azar 3
  • Ezatolah Asgharizadeh 4
1 Ph.D. Student of Industrial Management, Faculty of Management and Economics, Branch of Science and Research, Islamic Azad University, Tehran, Iran.
2 Associate Professor, Faculty of Management and Economics, Branch of Science and Research, Islamic Azad University, Tehran, Iran.
3 Professor, Management and Accounting Faculty, Tarbiat Modares University, Tehran, Iran.
4 Associate professor, Faculty of management, Tehran University, Tehran, Iran.
چکیده [English]

Predictive approaches play an important role in detecting events, controlling risks and reducing maintenance and repair costs. The purpose is to provide a model for predicting critical and prioritized risks based on data mining algorithms. Data mining method was planned based on the CRISP methodology. Data modeling has been done in two parts: "descriptive" and "predictive" data mining and the use of "clustering" and "classification" algorithms."Sillhouette index" is considered for clustering and the K-Means, Kohnen, Two Step algorithm is used; the best value is based on the K-Means algorithm. Silhouette is equal to 0.6446 with the number of clusters 5. Next, Neural Network Algorithms, C.5 tree, Nearest Neighbor and Support Vector have been used for classification. These techniques recognizing data classification patterns and their integration increases the amount of data learning. The results showed learning in 97.56% of the agreed data and the accuracy and validity of the combined model for data classification was estimated at 92.86%. Based on the results, 13 critical risks have been identified; "release of polluting gases and chemicals" and "lack of training and justification of contractors regarding the pipeline" have the highest and lowest priority, respectively.

کلیدواژه‌ها [English]

  • Risk Assessment
  • Maintenance
  • Modeling
  • Data Mining
  • Gas Pipeline

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