Daneshwar, Muhammad Amin (2016) Detection And Identification Of Stiction In Control Valves Based On Fuzzy Clustering Method. PhD thesis, Universiti Sains Malaysia.
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Abstract
Kehadiran geseran statik (stiction) dalam injap kawalan mengubah posisi injap daripada kedudukan asalnya dan menghasilkan perlakuan tak lelurus (jalur mati beserta jalur “stick” dan lompatan gelincir) dalam gelung kawalan. Ketaklelurusan ini memaksa gelung kawalan untuk berayun. Ayunan ini seterusnya menghasilkan kualiti produk yang rendah dan peningkatan dalam penggunaan tenaga. Oleh itu, pengesanan stiction pada masa yang tepat dalam gelung kawalan aliran, yang merupakan gelung kawalan penting dalam industri, adalah amat penting. Dalam kaedah-kaedah sebelum ini, apabila keujudan stiction telah dikesan, parameter stiction perlu dianggarkan (kuantifikasi) sebagai satu langkah untuk mengatasi masalah stiction. Walau bagaimanapun, penganggaran ini memerlukan pengorbanan dari segi masa dan usaha serta merupakan satu tugas yang mencabar. Dalam penyelidikan ini, untuk memperbaiki anggaran kovarians penggugusan kabur, sekaitan lelurus data adalah dikesan. Kemudian, satu matriks (yang mengandungi satu jujukan nombor rawak tak tersekait secara bersiri dengan min sifar dan varians terhingga) ditambah kepada matriks kovarians. Pengubahsuaian ini mengelakkan algoritma penggugusan kabur daripada menghadapi masalah berangka. Satu kaedah yang terhasil daripada ide bahawa dengan kewujudan stiction, akan menyebabkan pusat-pusat gugusan kawasan utama gelung kawalan aliran tersimpang daripada pusatnya, telah dicadangkan untuk mengesan sisihan (pengesanan). Selain itu, berdasarkan ide bahawa kecerunan garisan-garisan yang diperolehi daripada turutan pusat gugusan berkongsi beberapa sifat (dengan kehadiran stiction), satu indeks prestasi baru yang mengumpul sifat-sifat ini untuk membezakan punca ayunan (diagnosis) telah juga dicadangkan. Akhir sekali, sebagai alternatif kepada pengkuantitian stiction, satu model proses yang sesuai dengan stiction injap kawalan telah ditentukan (identifikasi) dengan mengkonfigurasi pengesan kabur. Model ini berupaya untuk menangkap (mengenal) semua dinamik yang bersesuaian bagi proses yang mengandungi stiction injap kawalan. Bilangan pengesanan betul yang diperolehi ialah 85%. Bukan sahaja masa pengesanan telah dapat dikurangkan kepada kurang daripada 1 saat (masa purata ialah 0.4505 saat), bahkan prestasi kaedah yang dicadangkan bagi pengesanan, diagnosis dan pengenalan stiction telah turut disahkan oleh kedua-dua data simulasi dan industri. ________________________________________________________________________________________________________________________ The presence of static friction (stiction) in control valves deviates the valve position from its origin and therefore produces a nonlinear behavior (dead band plus stick band and slip jump) in control loops. The nonlinearity forces control loops to oscillate. The oscillation results in poor product quality and increased energy consumption. The detection of stiction for flow control loops which form significant control loops in industry in a timely manner is of great importance. After the presence of stiction has been detected, in order to mitigate stiction problem, it is necessary to estimate stiction parameters (quantification) in the earlier methods. However, this estimation which requires huge investment of time and effort is a challenging task. In this study, in order to improve covariance estimation of fuzzy clustering, linearly correlation of data is detected. Then a matrix (which contains a sequence of serially uncorrelated random numbers with zero mean and finite variance) is added to covariance matrix. This modification prevents the fuzzy clustering algorithm from turning into numerical problem. A method, which gain benefits from the idea that in the presence of stiction, the cluster centers of main regions of flow control loops are deviated from their origin, is proposed to detect the deviation (detection). Furthermore, based on the idea that, the slopes of the lines obtained from successive cluster centers, share some properties (in the presence of stiction), a new performance index which collects these properties to distinguish cause of oscillation (diagnosis) is proposed. Finally as an alternative to stiction quantification, by configuring a fuzzy identifier, an appropriate model of process with control valve stiction is identified (identification). The identified model is able to capture (identify) all relevant dynamics of the process with control valve stiction. The number of correct detections is now 85%. Not only has the identification time been decreased to less than a second (i.e. average is 0.4505 seconds), the performance of the proposed methods of stiction detection, diagnosis and identification has also been confirmed by both simulation and industrial data.
| Item Type: | Thesis (PhD) |
|---|---|
| Additional Information: | Full text is available at http://irplus.eng.usm.my:8080/ir_plus/institutionalPublicationPublicView.action?institutionalItemId=3212 |
| Subjects: | T Technology T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering |
| Divisions: | Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Thesis |
| Depositing User: | Mr Mohd Jasnizam Mohd Salleh |
| Date Deposited: | 06 Sep 2018 08:55 |
| Last Modified: | 06 Sep 2018 08:55 |
| URI: | http://eprints.usm.my/id/eprint/41776 |
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