Combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft
Abstract
A review of current progress in the condition monitoring of wind turbine gearboxes and generator shaft is presented, including relevant work on gearboxes in other application areas, as an input to the design of a new continuous condition monitoring system with automated warnings based on a combination of vibrational and acoustic emission analysis. It is found that for wind turbines, existing reportage on vibrational monitoring is restricted to a few case histories whilst data on acoustic emission is even scarcer. In contrast, in the work described in this paper, combined vibration and acoustic emission monitoring was performed over a continuous period of 5 days on a wind turbine in actual service. The vibrational and acoustic emission signatures for a healthy wind turbine gearbox and generator shaft have been obtained as a function of wind speed and turbine power, for the full normal range of these operational variables. i.e. 5-25m/s and 0-350kW respectively. The signatures have been determined as a vital pre–requisite for the identification of abnormal signatures attributable to shaft and gearbox defects. Over the 5 days service period 813 files of signal output data from 2 vibrational sensors, 2 AE sensors, wind speed measurement and power measurement systems were obtained with 3 second periods for the speed and power data immediately followed by 1 second acquisition intervals for time dependent sensor data. Worst case standard deviations have been calculated for three kinds of time averaging of the sensor data. These standard deviations determine the minimum defect signal that could be detected within the defined time interval without false alarms in an automated warning system. As it is not acceptable to insert defects into a healthy turbine in service an example simulation is performed of the minimum size of defect that would be detectible with the current healthy signature data. The simulation calculation showed that for out of round defects in the bearing of the main shaft at the gearbox, rms defect amplitudes as small as 0.5mm would be detectable with a signal to noise ratio exceeding 3 (99.998% detection probability). With special digital processing techniques such as similarity analysis, applied to multiply repeated measurements during continuous monitoring, it is conceivable that the minimum detectable defect size could be reduced to 0.005mm, which is only 0.008% of the shaft radius.