کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
380241 1437429 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Pattern recognition in multivariate time series – A case study applied to fault detection in a gas turbine
ترجمه فارسی عنوان
تشخیص الگوی در سری زمانی چند متغیره؟ یک مطالعه موردی برای شناسایی خطا در یک توربین گاز استفاده شده است
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• Real case study comprising the fault detection in a gas turbine.
• Comprehensive method for pattern recognition associated to fault prediction in gas turbines.
• Results show the efficiency of the proposed approach for decision-making at operational level.
• The whole three step method presented is flexible and portable.
• An extended version of the FCM algorithm suitable for the clustering of multivariate time series is applied.

Advances in information technology, together with the evolution of systems in control, automation and instrumentation have enabled the recovery, storage and manipulation of a large amount of data from industrial plants. This development has motivated the advancement of research in fault detection, especially based on process history data. Although a large amount of work has been conducted in recent years on the diagnostics of gas turbines, few of them present the use of clustering approaches applied to multivariate time series, adopting PCA similarity factor (SPCA) in order to detect and/or prevent failures. This paper presents a comprehensive method for pattern recognition associated to fault prediction in gas turbines using time series mining techniques. Algorithms comprising appropriate similarity metrics, subsequence matching and fuzzy clustering were applied on data extracted from a Plant Information Management System (PIMS) represented by multivariate time series. A real case study comprising the fault detection in a gas turbine was investigated. The results suggest the existence of a safe way to start the turbine that can be useful to support the development of a dynamic system for monitoring and predicting the probability of failure and for decision-making at operational level.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 49, March 2016, Pages 10–18
نویسندگان
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