Sensorless detection of mechanical faults in electromechanical systems

Practical early fault detection and diagnosis systems must exhibit high level of detection accuracy, while exhibiting acceptably low false alarm rates. Further, it is desirable not to make use of add-on sensors, and require minimal information regarding the specific machine component parameters and design. In this paper the development and experimental demonstration of a sensorless detection and diagnosis system is presented for incipient faults in electromechanical systems, such as electric motors. The developed system uses recent developments in dynamic recurrent neural networks and wavelet signal processing. The signals utilized are only the motor currents and voltages, whereas the transient mechanical speed is estimated from these measurements using a recently developed speed filter. The effectiveness of the fault diagnosis system is demonstrated by detecting a wide range of mechanical faults at varying levels of deterioration. Furthermore, the ability of the diagnosis system to discriminate between false alarms and actual incipient failure conditions is demonstrated. Experimental test results from small machines, 2.2 kW, and large machines, 373 and 597 kW, are presented demonstrating the effectiveness of the proposed approach to scale-up with motor power rating.