Abstract
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 behaviourbehaviour. Future work will investigate the detection of more faults and using other classification algorithms.
Highlights
This paper presents a method to detect leaks in a compressed air system (CAS) using wavelet transforms and an artificial neural network
A pressure signal obtained from an air accumulator was analysed and processed using a wavelet transform
Experimental set-up A compressed air system installed in the Anglesea Building at the University of Portsmouth was used for data collection
Summary
This paper presents a method to detect leaks in a compressed air system (CAS) using wavelet transforms and an artificial neural network. Innovations in energy monitoring and management might help address this barrier. Such systems could automatically control energy consumption, identify malfunctions and recommend corrective actions [4]. Attempts to develop intelligent tools for energy management and fault detection in CAS have been investigated in [7],[8] and,[9]. Machine learning was investigated in [7] and [8] as a tool for detecting variations in energy usage patterns and it was concluded that these patterns could be associated with faults or irregular events. The suggested approach was useful for detecting intermittent leaks (i.e.: leaks sensed only when a particular defective tool is activated)
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