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Search Results (2)

Search Parameters:
Keywords = Frisch scheme

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21 pages, 2299 KiB  
Review
Overview of Identification Methods of Autoregressive Model in Presence of Additive Noise
by Dmitriy Ivanov and Zaineb Yakoub
Mathematics 2023, 11(3), 607; https://doi.org/10.3390/math11030607 - 26 Jan 2023
Cited by 4 | Viewed by 3032
Abstract
This paper presents an overview of the main methods used to identify autoregressive models with additive noises. The classification of identification methods is given. For each group of methods, advantages and disadvantages are indicated. The article presents the simulation results of a large [...] Read more.
This paper presents an overview of the main methods used to identify autoregressive models with additive noises. The classification of identification methods is given. For each group of methods, advantages and disadvantages are indicated. The article presents the simulation results of a large number of the described methods and gives recommendations on choosing the best methods. Full article
(This article belongs to the Special Issue New Trends on Identification of Dynamic Systems)
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24 pages, 519 KiB  
Article
Residual Generator Fuzzy Identification for Wind TurbineBenchmark Fault Diagnosis
by Silvio Simani, Saverio Farsoni and Paolo Castaldi
Machines 2014, 2(4), 275-298; https://doi.org/10.3390/machines2040275 - 27 Nov 2014
Cited by 11 | Viewed by 6346
Abstract
In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the [...] Read more.
In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the reliability of the available measurements. In this work, adata-driven strategy relying on fuzzy models is presented, in order to build a fault diagnosissystem. Fuzzy theory jointly with the Frisch identification scheme for errors-in-variablemodels is exploited here, since it allows one to approximate unknown models and manageuncertain data. Moreover, the use of fuzzy models, which are directly identified from thewind turbine measurements, allows the design of the fault detection and isolation module.It is worth noting that, sometimes, the nonlinearity of a wind turbine system could lead toquite complex analytic solutions. However, IF-THEN fuzzy rules provide a simpler solution,important when on-line implementations have to be considered. The wind turbine benchmarkis used to validate the achieved performances of the suggested fault detection and isolationscheme. Finally, comparisons of the proposed methodology with respect to different faultdiagnosis methods serve to highlight the features of the suggested solution. Full article
(This article belongs to the Special Issue Machinery Diagnostics and Prognostics)
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