Gramian angular field imaging and texture analysis for identifying instability precursors in compressor unsteady flows
Bingjie Li, Chen Huang, Teng Zhou, Wenqiang Zhang, Lei Zhang, Ben Zhao
Abstract
We present an image-based approach for early detection of surge precursors in a centrifugal compressor. One-dimensional pressure signals are mapped to Gramian angular field images, from which Gray Level Co-occurrence Matrix correlation is computed as the primary indicator using fixed windowing and gray-level quantization. On a single test rig at 60 000 and 70 000 rpm, the correlation trace rises prior to measured surge onset and follows the approach–surge–recovery evolution (e.g., a surge interval near 19.41–19.51 s at 60 000 rpm), enabling a baseline-referenced threshold for monitoring. To stabilize short-window fluctuations, a lightweight exponentially weighted moving average (α = 0.1) produces smoother yet responsive trajectories without resorting to black-box training. In the stable operating map, correlation exhibits a V-shaped variation with decreasing mass flow—coherent at overload, weakest near peak efficiency, and strengthening again as surge is approached—providing a physically interpretable trend. Within this scope, the study is a compressor-focused proof-of-concept; extension to other unsteady-flow problems is plausible but will require problem-specific tuning and independent validation.