Early detection of thermoacoustic instabilities in a cryogenic rocket thrust chamber using combustion noise features and machine learning

We present a data-driven method for the early detection of thermoacoustic instabilities. Recurrence quantification analysis is used to calculate characteristic combustion features from short-length time series of dynamic pressure sensor data. Features like recurrence rate are used to train support vector machines to detect the onset of instability a few hundred milliseconds in advance. The performance of the proposed method is investigated on experimental data from a representative LOX/H2 research thrust chamber. In most cases, the method is able to timely predict two types of thermoacoustic instabilities on test data not used for training. The results are compared with state-of-the-art early warning indicators. High-amplitude pressure oscillations known as combustion instability are an issue for all chemical propulsion systems. Combustion instabilities are particularly problematic for rocket thrust chambers because of their high-energy release rates. Specifically, thermoacoustic oscillations are major hazards and difficult to predict. An important question is whether features of combustion noise can be used to construct reliable early warning signals for representative rocket thrust chambers. Among other things, instability precursors are needed for active combustion control systems. In this study, we use the recurrence quantification analysis and well-known machine learning models to construct an early warning signal that should be able to predict high-amplitude instabilities a few hundred milliseconds in advance. The performance of the proposed method is investigated on experimental data from a representative LOX/H2 research thrust chamber. We describe the test case in detail and also evaluate other measures like the Hurst exponent