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Sensor fault detection and minimum detectable fault analysis for dynamic point-the-bit rotary steerable system.
Wang, Weiliang; Geng, Yanfeng; Sun, Jian; Xu, Huijie; Sheng, Li.
Afiliación
  • Wang W; College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China.
  • Geng Y; College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China. Electronic address: gengyf@upc.edu.cn.
  • Sun J; College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China.
  • Xu H; College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China.
  • Sheng L; College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China.
ISA Trans ; 127: 108-119, 2022 Aug.
Article en En | MEDLINE | ID: mdl-34972545
ABSTRACT
In this paper, the sensor fault detection problem considering the drilling disturbances is studied for the dynamic point-the-bit rotary steerable system. Firstly, the DPRSS is modeled as a linear system with the drilling disturbances, including unknown inputs, measurement noises, and model perturbations. Then, a finite-frequency zonotopic fault detection observer is proposed. The finite-frequency range H- performance and the P-radius criterion are considered to design the observer gains such that the residuals are sensitive to sensor faults and robust against the drilling disturbances simultaneously. Subsequently, the calculation method of minimum detectable faults is presented for the proposed sensor fault detection mechanism. Finally, simulations and experiments are presented to illustrate the effectiveness of the proposed methods.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: ISA Trans Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: ISA Trans Año: 2022 Tipo del documento: Article País de afiliación: China