Compressor Stall Warning Using Nonlinear Feature Extraction Algorithms

Stall is a type of flow instability in compressors which sets the low flow limit for compressor operations. As a result of the damaging consequences, extensive research has been put toward stall inception, stall detection, and stall control. However, there is limited progress in developing a reliable stall warning or effective stall suppression system, which motivates the work presented in this paper. This paper focuses on the small nonlinear disturbances prior to deep surge and introduces a new approach to identify these disturbances using nonlinear feature extraction algorithms including: phase-reconstruction of time-serial signals and evaluation of a parameter called approximate entropy. The method is different from the well-known stall warning techniques in the time domain including the correlation measure method introduced by Dhingra et al. and the ensemble-average method introduced by Young et al. The analysis of the new method is performed in phase space using the approximate entropy parameter. Approximate entropy is a measure of the amount of regularity and unpredictability of fluctuations in time-series data and was first introduced by Pincus. In general, a time series with more repetitive patterns of fluctuations renders smaller approximate entropy and vice versa. Furthermore, the method is applied to a high-speed centrifugal compressor, which experienced unexpected rotating stall during speed transients, and a multi-stage axial compressor, with both modal- and spike- type of stall. For both compressors, the signals from casing-mounted transducers were first reconstructed in the phase domain. Then, the approximate entropy for the phase-reconstructed signal was evaluated. In both cases, the appearance of nonlinear disturbances, in terms of spikes in approximate entropy, occur prior to stall. The concurrent spike in approximate entropy with the occurrence of the pressure disturbance shows that the parameter is capable of capturing small disturbances in a compression system and also indicates the potential of using the approximate entropy parameter for stall warning in aero engines. This is the first use of approximate entropy for the purpose of stall warning in compressors in the open literature. As with other stall warning techniques, the intelligent choice of several parameters must be exercised. To implement approximate entropy, there are four parameters involved including the number of data, embedding dimension, time delay, and radius of similarity. The influence of these four parameters on the effectiveness of approximate entropy for disturbance extraction was explored. Additionally, considerations and guidelines for selection of each individual parameter were also provided. To summarize, this paper introduces a new approach for identifying small pre-stall or surge disturbances using nonlinear feature extraction algorithms. Analysis of the unsteady pressure acquired at two compressor research facilities shows the potential of using approximate entropy for stall warning in gas turbine engines. The work presented in the paper serves as a foundation for future work on stall warning using nonlinear algorithms.