کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4942770 | 1437421 | 2017 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Early fault detection of engineering systems allows early warnings of anomalies and provides time to initiate proactive mitigation actions before the anomaly has developed to a problem that either requires extensive maintenance or affects the productivity of the system. In this paper, a new fault detection method using signal reconstruction based on Auto-Associative Extreme Learning Machines (AAELM) is proposed. AAELM are applied for fault detection on an artificially generated dataset to test the performance of the algorithm under controlled conditions and a real case study based on condition monitoring data from a combined-cycle power plant compressor. The performance of AAELM is compared to that of two other commonly used signal reconstruction methods: Auto-Associative Kernel Regression (AAKR) and Principal Component Analysis (PCA). The results from the two case studies demonstrate that AAELM achieve a smaller reconstruction error, shorter detection delay, lower spillover and a higher distinguishability compared to AAKR and PCA on the evaluated datasets. The obtained results are generalized to elaborate guidelines for industrial users for selecting suitable signal reconstruction algorithms based on their specific requirements and boundary conditions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 57, January 2017, Pages 105-117
Journal: Engineering Applications of Artificial Intelligence - Volume 57, January 2017, Pages 105-117
نویسندگان
Yang Hu, Thomas Palmé, Olga Fink,