کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7107986 | 1460590 | 2015 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach](/preview/png/7107986.png)
چکیده انگلیسی
This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Annual Reviews in Control - Volume 40, 2015, Pages 59-69
Journal: Annual Reviews in Control - Volume 40, 2015, Pages 59-69
نویسندگان
Rosa M. Fernandez-Canti, Joaquim Blesa, Sebastian Tornil-Sin, Vicenç Puig,