Reducing risk and increasing reliability and safety of compressed air systems by detecting patterns in pressure signals

This paper investigates the design of a classifier that effectively identifies undesired events by detecting patterns in the pressure signal of a compressed air system using a continuous wavelet transform. The pressure signal of a compressed air system carries useful information about operational events. These events form patterns that can be used as ‘signatures’ for event detection. Such patterns are not always apparent in the time domain and hence the signal was transformed to the time-frequency domain. Data was collected using an industrial compressed air system with load/unload control. Three different operating modes were considered: idle, tool activation and faulty. The wavelet transforms of the pressure signal revealed unique features to identify events within each mode. A neural network classifier was created to detect faulty compressed air system behaviour. Future work will investigate detection of more faults and using other classification algorithms.