Singular spectrum analysis as a tool for early detection of centrifugal compressor flow instability

Abstract

Centrifugal compressor machinery is subject to a potentially damaging phenomenon called surge at low mass flow rates. This effect may be preceded by a phenomena known as inlet recirculation — a flow reversal upstream of the impeller. A methodology to isolate inlet recirculation as a characteristic feature for monitoring of centrifugal compressor instability is presented in this study. The methodology is based on a nonparametric time series analysis technique called as singular spectrum analysis (SSA). SSA decomposes a signal into a number of Reconstructive Components (RCs), from which data trends and oscillatory components may be extracted. The frequency spectra of each RC and their relative contributions to the reconstruction of the original signal were examined and comparisons were made with spectral maps in existing literature. Individual and independent RCs were chosen to construct a compressor’s instability monitoring system. Additionally, the performance of SSA was determined by the Window length parameter. The effect of modification of this parameter was also studied, and the various viable choices of component for the basis of inlet recirculation diagnosis were considered. The methodology was implemented in pressure dynamical signals measured in an experimental centrifugal compressor rig. High frequency pressure measurements were taken at a number of flow conditions and locations within the compressor. The results demonstrated the potential of a methodology based on SSA to identify and extract oscillatory components with information about the local effect of inlet recirculation and eventually successfully monitor centrifugal compressor’s instability.