Early detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance, and improved operational efficiency of induction motors. At the same time, reducing the probability of false alarms increases the confidence of equipment owners in this new technology. In this paper, a model-based fault diagnosis system recently proposed by the authors for induction motors is experimentally compared for fault detection and false alarm performance with a more traditional signal-based motor fault estimator. In addition to the nameplate information required for the initial set-up, the proposed model-based fault diagnosis system uses measured motor terminal currents and voltages, and motor speed. The motor model embedded in the diagnosis system is empirically obtained using dynamic recurrent neural networks, and the resulting residuals are processed using wavelet packet decomposition. The effectiveness of the model-based diagnosis system in detecting the most widely encountered motor electrical and mechanical faults, while minimizing the impact of false alarms resulting from power supply and load variations, is demonstrated through extensive testing with staged motor faults. The model-based fault diagnosis system is scalable to motors of different power ratings and it has been successfully tested with fault data from and induction motors.
Skip Nav Destination
e-mail: Kim_Kyusung@htc.honeywell.com
e-mail: a-parlos@tamu.edu
Article navigation
March 2003
Technical Papers
Reducing the Impact of False Alarms in Induction Motor Fault Diagnosis
Kyusung Kim,
e-mail: Kim_Kyusung@htc.honeywell.com
Kyusung Kim
Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123
Search for other works by this author on:
Alexander G. Parlos
e-mail: a-parlos@tamu.edu
Alexander G. Parlos
Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123
Search for other works by this author on:
Kyusung Kim
Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123
e-mail: Kim_Kyusung@htc.honeywell.com
Alexander G. Parlos
Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123
e-mail: a-parlos@tamu.edu
Contributed by the Dynamic Systems and Control Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received by the ASME Dynamic Systems and Control Division, April 2001, final revision, September 2002. Associate Editor: J. Tu.
J. Dyn. Sys., Meas., Control. Mar 2003, 125(1): 80-95 (16 pages)
Published Online: March 10, 2003
Article history
Received:
April 1, 2001
Revised:
September 1, 2002
Online:
March 10, 2003
Citation
Kim, K., and Parlos, A. G. (March 10, 2003). "Reducing the Impact of False Alarms in Induction Motor Fault Diagnosis ." ASME. J. Dyn. Sys., Meas., Control. March 2003; 125(1): 80–95. https://doi.org/10.1115/1.1543550
Download citation file:
Get Email Alerts
Integral Sliding Mode Disturbance Observer-Based Preview Repetitive Control
J. Dyn. Sys., Meas., Control (July 2024)
Global-Position Tracking Control for Multi-Domain Bipedal Walking with Underactuation
J. Dyn. Sys., Meas., Control
Surge-Elimination Strategy for Aero-Engine Transient Control
J. Dyn. Sys., Meas., Control (July 2024)
Safe Reinforcement Learning-Based Balance Control for Multi-Cylinder Hydraulic Press
J. Dyn. Sys., Meas., Control (July 2024)
Related Articles
Measurement Selections for Multicomponent Gas Path Diagnostics Using Analytical Approach and Measurement Subset Concept
J. Eng. Gas Turbines Power (November,2011)
Degradation Assessment and Fault Modes Classification Using Logistic Regression
J. Manuf. Sci. Eng (November,2005)
Commutation Sparking Image Monitoring for DC Motor
J. Manuf. Sci. Eng (April,2012)
Nonlinear Engine Component Fault Diagnosis From a Limited Number of Measurements Using a Combinatorial Approach
J. Eng. Gas Turbines Power (July,2003)
Related Proceedings Papers
Related Chapters
Vibration Monitoring for Fault Diagnosis in Rotating Machinery Using Wavelet Transform
International Conference on Advanced Computer Theory and Engineering, 4th (ICACTE 2011)
Study on Aero-Engine Rotor Fault Diagnosis Based on Flight Data
International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
Design of Fault Diagnosis System of Industrial Boiler Based on MATLAB
International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)