Detail Model-based Fault Detection and Diagnosis using Parameter Estimation and Fuzzy Inference   Oleh:  Abidin, Mohamad Shukri Zainal ; Khalid, Marzuki ; Yusof, Rubiyah ; Amin, Mohd Shamsuddin Jenis: Article from Article Dalam koleksi: Final Program and Book of Abstracts: The 4th Asian Control Conference, September 25-27, 2002 (Sep. 2002), page 1373-1378. Topik: Fault Detection; Diagnosis; Fuzzy Inference Fulltext: AC021738.PDF (263.63KB) Isi artikel Fault detection and diagnosis have been widely applied in many industrial applications. Much of these techniques have been based on the hardware redundancy or the model-based fault diagnostic schemes. Recently, artificial intelligence (AI) techniques have been found to be suitable for fault detection and diagnosis and a variety of techniques have been proposed. In this paper, we propose a fault detection and diagnostic scheme based on the model-based approach. Due to its reliability, the popular recursive least squares parameter estimation technique is used for the modeling of the plant, then, fuzzy inferencing is used for the interpretation of the fault. The proposed method is experimented on a d. c. motor servo trainer. Several faults have been identified on the system and fuzzy inferencing is used for the interpretation of the faults. The faults are then simulated on the motor and experiments are carried out to diagnose the types of faults. The experiments have shown the proposed technique is viable for real-time application. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Kembali

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