This paper proposes a framework for detecting mechanical degradation online and assessing its effect on the performance of industrial compressors. It consists of a model of the machine in undegraded condition and of a degradation adaptive model. The proposed methodology for online degradation detection differentiates itself from those found in the literature as the undegraded model is not linearized and ambient/inlet conditions are explicitly taken into account. The degradation is modelled through adaptive parameters which are estimated and updated online through the solution of a constrained minimization problem within a moving window. It uses available process measurements of flow, pressures, temperatures and composition. The update of the parameters guarantees the model accuracy and it permits the estimation of the effects of mechanical degradation away from the compressor running line.
The performance monitoring framework has been successfully applied on an industrial air centrifugal compressor. It was found that after 3250 hours of operation from the previous maintenance the efficiency and the pressure ratio had dropped approximately 5.5% and 2.5% of their respective undegraded values. Furthermore, it was found that the performance deviations from the baseline depend from the position of the operative point in the performance map. In fact, the pressure ratio drop was lower (2%) and efficiency drop was higher (6%) for lower inlet guide vanes opening whereas pressure ratio drop was higher (3%) and efficiency drop was lower (1.6%) for higher inlet guide vane opening.
