Abstract:
The acoustics, vibration, tacho voltage (tacho is a part of the universal motor used to get a feedback from the motor to control the speed), feeding voltage and the supply current signals reflect the health of the motor. When there is a diversion from the normal operating situation these signals change their characteristics. By analyzing these signals and comparing the results with the reference data obtained from the non-defective motors, the anomalous behaviour can be detected. These signals can be used individually or together in the fault detection of the motors. A number of techniques have been developed which monitor certain parameters of the motors, allowing its health to be determined. These monitoring techniques are known as Machine Condition Monitoring. Condition monitoring technique can often be separated into two main categories, those being invasive and those being non-invasive. Invasive monitoring, as the name suggests, involves the disassembly of the motor in question, whereas the non-invasive techniques allow the health of the motor to be obtained while the motor is still in its normal operation. Modem signal processing techniques allow us to see much more deeply into the operations of plants and processes, particularly when we base the prpcedure on a fundamental understanding of the mechanics of operation of the machine. There are var:ious kinds of signal processing tools to analyze the signals obtained from the motors. In this work we aim to detect the noise problems and identify the various faults in the universal motors by monitoring and analyzing the sound and vibration signals. This method contributes a cheap approach to fault identification of the sound and vibration signals coming from the defective motors, that could originate from a defective brush and/or commutator, defective bearings, an unbalanced rotor or from a tom-folio of the rotor) are compared against the sound database of non-defective motors. For fault identification purposes extensive use of periodogram spectra was made. Feature extracted from spectra were assessed as for their discriminatory power. since rotating machinery gives rise to resonant spectra, spectral peaks were found to be the best set of evidence to classify between motor fault types. Preliminary statistical results with spectral peaks indicate that it is feasible to develop a noninvasive tool based on acoustics / vibration data for fault classification.
