Document Type : Review Article

Authors

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

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

Abstract

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.

Keywords

[1] P. Samanipour and J. Poshtan, "Electro pump modeling using laboratory system data," in Power Electronics and Drive Systems Technologies Conference (PEDSTC), 2016 7th, 2016, pp. 111-115.
[2] N. S. Nokhodberiz and J. Poshtan, "Belief consensus–based distributed particle filters for fault diagnosis of non-linear distributed systems," Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 228, no.3, pp. 123-137, 2013..
[3] S. Mondal, G. Chakraborty, and K. Bhattacharyy, "LMI approach to robust unknown input observer design for continuous systems with noise and uncertainties," International Journal of Control, Automation and Systems, vol. 8, pp. 210-219, 2010.
[4] V. F. Pires, J. Martins, and A. Pires, "Eigenvector/eigenvalue analysis of a 3D current referential fault detection and diagnosis of an induction motor," Energy conversion and management, vol. 51, pp. 901-907, 2010.
[5] A. Lemos, W. Caminhas, and F. Gomide, "Adaptive fault detection and diagnosis using an evolving fuzzy classifier," Information Sciences, vol. 220, pp. 64-85, 2013.
[6] H. Jafari and J. Poshtan, "Fault isolation and diagnosis of induction motor based on multi-sensor data fusion," in Power Electronics, Drives Systems & Technologies Conference (PEDSTC), 2015 6th, 2015, pp. 269-274.
[7] Z. Tian, L. Wong, and N. Safaei, "A neural network approach for remaining useful life prediction utilizing both failure and suspension histories," Mechanical Systems and Signal Processing, vol. 24, pp. 1542-1555, 2010.
[8] N. Sakthivel, V. Sugumaran, and S. Babudevasenapati, "Vibration based fault diagnosis of monoblock centrifugal pump using decision tree," Expert Systems with Applications, vol. 37, pp. 4040-4049, 2010.
[9] J. Alonso, M. Ferrer, and C. Travieso, "Fault diagnosis using audio and vibration signals in a circulating pump," in Journal of Physics: Conference Series, 2012, p. 012135.
[10] N. Sadaghzadeh N. J. Poshtan, A. Wagner, E. Nordheimer, and E. Badreddin, "Cascaded Kalman and particle filters for photogrammetry based gyroscope drift and robot attitude estimation," ISA transactions, vol. 53, pp. 524-532, 2014.
[11] F. Aguilera, M. Pablo, and C. H. De Angelo, "Behavior of electric vehicles and traction drives during sensor faults," in Industry Applications (INDUSCON), 2012 10th IEEE/IAS International Conference on, 2012, pp. 1-7.
[12] S. K. Kommuri, J. J. Rath, K. C. Veluvolu, M. Defoort, and Y. C. Soh, "Decoupled current control and sensor fault detection with second-order sliding mode for induction motor," IET Control Theory & Applications, vol. 9, pp. 608-617, 2015.
[13] A. Raisemche, M. Boukhnifer, C. Larouci, and D. Diallo, "Two active fault-tolerant control schemes of induction-motor drive in EV or HEV," IEEE Transactions on Vehicular Technology, vol. 63, pp. 19-29, 2014.
[14] X. Shi and M. Krishnamurthy, "Survivable operation of induction machine drives with smooth transition strategy for EV applications," IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 2, pp. 609-617, 2014.
[15] N. M. Freire, J. O. Estima, and A. J. M. Cardoso, "A new approach for current sensor fault diagnosis in PMSG drives for wind energy conversion systems," IEEE Transactions on Industry Applications, vol. 50, pp. 1206-1214, 2014.
[16] X. Zhang, G. Foo, M. D. Vilathgamuwa, K. J. Tseng, B. S. Bhangu, and C. Gajanayake, "Sensor fault detection, isolation and system reconfiguration based on extended Kalman filter for induction motor drives," IET Electric Power Applications, vol. 7, pp. 607-617, 2013.
[17] T. A. Najafabadi, F. R. Salmasi, and P. Jabehdar-Maralani, "Detection and isolation of speed-, DC-link voltage-, and current-sensor faults based on an adaptive observer in induction-motor drives," IEEE Transactions on Industrial Electronics, vol. 58, pp. 1662-1672, 2011.
[18] F. Aguilera, P. de la Barrera, C. De Angelo, and D. E. Trejo, "Current-sensor fault detection and isolation for induction-motor drives using a geometric approach," Control Engineering Practice, vol. 53, pp. 35-46, 2016.
[19] N. Sadeghzadeh-Nokhodberiz, J. Poshtan, A. Wagner, E. Nordheimer, and E. Badreddin, "Distributed observers for pose estimation in the presence of inertial sensory soft faults," ISA transactions, vol. 53, pp. 1307-1319, 2014.
[20] M. Sepasi, "Fault monitoring in hydraulic systems using unscented Kalman filter," University of British Columbia, 2007.
[21] D. Gebre-Egziabher, "Design and performance analysis of a low-cost aided dead reckoning navigator," Citeseer, 2004.
[22] G. Welch and G. Bishop, "An introduction to the kalman filter. Department of Computer Science, University of North Carolina," ed: Chapel Hill, NC, unpublished manuscript, 2006.