Improved condition monitoring using fast-oscillating measurements

In this paper, a technique of merging typical process data with variables containing fast periodic oscillations is proposed for the purpose of detecting faults in industrial systems working under variable operating conditions. Analysing windows of the fast-oscillating signals allowed key features to be extracted from the data at the same rate at which the process variables are sampled. This allows the fusion of both types of data acquired at different sampling rates in a single data matrix. The data is then analysed using canonical variate analysis (CVA) looking for deviations in any parameter that can point at a fault in the system. The dynamic characteristics of CVA allow the detection and diagnosis of faults in systems working under variable operating conditions. This approach was tested using experimental data acquired from a compressor test rig where the compressor surge process fault. Results suggest that the combination of both types of data can effectively improve the detectability of faults in systems working under variable operating conditions.

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