TY - GEN
T1 - Evaluation of feature calculation methods for electromechanical system diagnosis
AU - Delgado, M.
AU - García, A.
AU - Ortega, J. A.
PY - 2011
Y1 - 2011
N2 - The use of intelligent machine health monitoring schemes is increasing in critical applications as traction tasks in the transport sector. The high diagnosis capability and reliability required in these systems are being supported by intelligent classification algorithms. These classifiers use calculated features from the system to perform the diagnosis. In this context, different features calculation methods can be applied to characterize the system condition obtaining different classification results. The aim of this work is based on diagnosis capabilities evaluation of the main features calculation methods: statistical features from time, statistical features from frequency, time-frequency distributions and signal decomposition techniques. The features capabilities are quantitatively evaluated by two parameters: the classification accuracy and the discriminant coefficient. Experimental results are obtained from an electromechanical actuator under different diagnosis requirements: from single fault to combined faults detection under stationary and non-stationary speed and torque conditions.
AB - The use of intelligent machine health monitoring schemes is increasing in critical applications as traction tasks in the transport sector. The high diagnosis capability and reliability required in these systems are being supported by intelligent classification algorithms. These classifiers use calculated features from the system to perform the diagnosis. In this context, different features calculation methods can be applied to characterize the system condition obtaining different classification results. The aim of this work is based on diagnosis capabilities evaluation of the main features calculation methods: statistical features from time, statistical features from frequency, time-frequency distributions and signal decomposition techniques. The features capabilities are quantitatively evaluated by two parameters: the classification accuracy and the discriminant coefficient. Experimental results are obtained from an electromechanical actuator under different diagnosis requirements: from single fault to combined faults detection under stationary and non-stationary speed and torque conditions.
KW - Fault diagnosis
KW - Frequency domain analysis
KW - Nearest Neighbor searches
KW - Neural Networks
KW - Permanent magnet motors
KW - Stator currents
KW - Time domain analysis
KW - Time-Frequency analysis
KW - Vibrations analysis
UR - https://www.scopus.com/pages/publications/81255146910
U2 - 10.1109/DEMPED.2011.6063669
DO - 10.1109/DEMPED.2011.6063669
M3 - Conference contribution
AN - SCOPUS:81255146910
SN - 9781424493036
T3 - SDEMPED 2011 - 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives
SP - 495
EP - 502
BT - SDEMPED 2011 - 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives
T2 - 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2011
Y2 - 5 September 2011 through 8 September 2011
ER -