Model-based sensor fault detection to brushless DC motor using Luenberger observer

Fault detection (FD) techniques play a crucial role at increasing the reliability, maintainability and safety for engineering applications. Meanwhile, Brushless Direct Current (BLDC) motor is a main integrated part for engineering systems in several mechatronics and robotics applications. Therefore, this work presents a model-based sensor FD approach. The proposed approach is tailored for detecting speed sensor faults at BLDC motor through exploiting Luenberger observer. Several experiments are conducted in order to validate the performance of the proposed approach. During the conducted experiments different faults types are applied to the motor in order to test the proposed FD approach. The experimental results demonstrate the ability of the proposed approach to detect the time and size of the sensor faults.

[1]  C. W. Chan,et al.  Application of Fully Decoupled Parity Equation in Fault Detection and Identification of DC Motors , 2006, IEEE Transactions on Industrial Electronics.

[2]  Rolf Isermann,et al.  Application of model-based fault detection to a brushless DC motor , 2000, IEEE Trans. Ind. Electron..

[3]  Marcin Kaminski,et al.  A Modified Fuzzy Luenberger Observer for a Two-Mass Drive System , 2015, IEEE Transactions on Industrial Informatics.

[4]  K. Jezernik,et al.  Identifying dynamic model parameters of a BLDC motor , 2008, Simul. Model. Pract. Theory.

[5]  S Padmakumar,et al.  A Comparative Study into Observer based Fault Detection and Diagnosis in DC Motors: Part-I , 2009 .

[6]  Alkan Alkaya,et al.  Luenberger observer-based sensor fault detection: online application to DC motor , 2014 .

[7]  Rolf Isermann,et al.  Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems , 2011 .

[8]  Jerome Jovitha,et al.  Comparison of four state observer design algorithms for MIMO system , 2013 .

[9]  M. Hernandez,et al.  A simple fault detection of induction motor by using parity equations , 2011, 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives.

[10]  K. Lias,et al.  Fault Detection using Dynamic Parity Space Approach , 2012, 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia.

[11]  Mouna Ben Hamed,et al.  Application of parity space approach in fault detection of DC motors , 2012, 2012 First International Conference on Renewable Energies and Vehicular Technology.

[12]  P. Frank,et al.  Survey of robust residual generation and evaluation methods in observer-based fault detection systems , 1997 .

[13]  K. Szabat,et al.  Design and analysis of the luenberger observers for three-inertia system , 2009 .

[14]  Oludayo Oguntoyinbo PID control of brushless DC motor and robot trajectory planning simulation with MATLAB®/SIMULINK® , 2009 .

[15]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[16]  Eliezer Colina-Morles,et al.  Generalized Luenberger observer-based fault-detection filter design: an industrial application , 2000 .

[17]  Jing Yang,et al.  Fault detection and diagnosis of permanent-magnet DC motor based on parameter estimation and neural network , 2000, IEEE Trans. Ind. Electron..