A Machine Learning Approach for Solid Rocket Motor Data Analysis and Virtual Sensor Development

The characterization of solid rocket motors is imperative for understanding the fundamental behavior of any solid booster powered system. Obtaining data related to combustion of solid propellants is complicated by the high pressures and harsh chemical conditions inside of the motor casing and generated by the plume environment. Using a combination of machine learning with acquired measurements as independent inputs, it is possible to create “virtual sensors” that will provide critical information unavailable due to the inability of sensor placement within the combustion chamber or plume itself. This approach will supplement physically acquired data during ground static testing of solid rocket motors, as well as increase performance measurement above the limited numbered of digital flight instrumentation. Data acquisition for the virtual sensing effort is performed using a solid booster static test stand developed at GTRI. In order to capture the plume characterization with physical sensors, the test instrumentation suite includes axial thrust, on-board acceleration and casing temperature and external imaging, temperature, and acoustics measurements. The machine learning approach focuses on developing anomaly detection, shock detection, and sensor reconstruction solutions through virtual sensing and image processing algorithms for predictive performance analysis of a solid booster. Results show the pre-processing of experimental data, including the resulting dynamics during a test fire, and the ability to recreate different sensor signals based on their inherent behaviors. Additionally, the anomaly detection approach gives autonomous detection of off-normal events and different methods of edge detection were used to identify shocks within the plume.