Sensor Fault Detection and Identification in an Electro-pump System using Extended Kalman Filter

سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 143

فایل این مقاله در 13 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

JR_MJEE-15-4_002

تاریخ نمایه سازی: 23 بهمن 1401

چکیده مقاله:

In this article, the issue of sensor fault detection and identification with sensory information is considered. This is due to the dependence of successful Fult Detection (FD) method on correct sensory measurements that suffer from various soft sensory faults such as bias, drift, scaling factor, and hard faults that can be detected independently. They are not detectable but can be combined with other sensors. To solve this issue, firstly, a state space model for pump subsystem was constructed using the electrical simulation method. Then, the sensory soft faults are modeled and amplified to electro-pump state space model.  Both system states and amplified sensory soft faults are then estimated using an Extended Kalman filter (EKF) in which nonlinear model of the induction motor is linearized around the estimated states. Information of current, angular velocity (encoder) and pressure sensors are  melted for this goal. The efficiency of the method is firstly evaluated through simulation and then experimental results are provided from our laboratory setup. Measured volume currents, flow, and pressure are compared with simulated signals, and results show that the proposed model is able to successfully describe the laboratory system with good precision. These results show that the model can describe the electro-pump dynamic with good precision.

نویسندگان

Monir Rezaee

Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.

Nargess Sadeghzadeh Nokhobberiz

Department of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran.

Javad Poshtan

Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :
  • P. Samanipour and J. Poshtan, "Electro pump modeling using laboratory ...
  • N. S. Nokhodberiz and J. Poshtan, "Belief consensus–based distributed particle ...
  • S. Mondal, G. Chakraborty, and K. Bhattacharyy, "LMI approach to ...
  • V. F. Pires, J. Martins, and A. Pires, "Eigenvector/eigenvalue analysis ...
  • A. Lemos, W. Caminhas, and F. Gomide, "Adaptive fault detection ...
  • H. Jafari and J. Poshtan, "Fault isolation and diagnosis of ...
  • Z. Tian, L. Wong, and N. Safaei, "A neural network ...
  • N. Sakthivel, V. Sugumaran, and S. Babudevasenapati, "Vibration based fault ...
  • J. Alonso, M. Ferrer, and C. Travieso, "Fault diagnosis using ...
  • N. Sadaghzadeh N. J. Poshtan, A. Wagner, E. Nordheimer, and ...
  • F. Aguilera, M. Pablo, and C. H. De Angelo, "Behavior ...
  • S. K. Kommuri, J. J. Rath, K. C. Veluvolu, M. ...
  • A. Raisemche, M. Boukhnifer, C. Larouci, and D. Diallo, "Two ...
  • X. Shi and M. Krishnamurthy, "Survivable operation of induction machine ...
  • N. M. Freire, J. O. Estima, and A. J. M. ...
  • X. Zhang, G. Foo, M. D. Vilathgamuwa, K. J. Tseng, ...
  • T. A. Najafabadi, F. R. Salmasi, and P. Jabehdar-Maralani, "Detection ...
  • F. Aguilera, P. de la Barrera, C. De Angelo, and ...
  • N. Sadeghzadeh-Nokhodberiz, J. Poshtan, A. Wagner, E. Nordheimer, and E. ...
  • M. Sepasi, "Fault monitoring in hydraulic systems using unscented Kalman ...
  • D. Gebre-Egziabher, "Design and performance analysis of a low-cost aided ...
  • G. Welch and G. Bishop, "An introduction to the kalman ...
  • نمایش کامل مراجع