Critical measurement parameters estimation in liquid rocket engine using LSTM-based soft sensor

Abstract

Liquid Rocket engines (LREs) need to be hot tested on the ground, in order to make them flight-worthy. Ground hot tests are critical, expensive, and involve considerable human efforts in acquiring the critical measurement parameters. Measurement sensors are prone to failure due to the hazardous propellant environment and are vulnerable to noise (internal and external) during the LRE ground hot tests. The usage of redundant sensors is not a viable solution for space applications due to cost, power, weight, and space constraints. Data-driven Soft Sensors (SS) are being used in different industrial processes. SS can provide an effective alternative and economical solution for space applications. The objective of the proposed work is to implement a suitable data-driven soft sensor for estimating three critical measurement parameters namely, turbine speed, chamber pressure (Pc), and propellant flow rate of LRE. Long Short-Term Memory (LSTM) networks are capable of learning order dependence in sequence prediction problems and are well-suited to make predictions based on time series data. As the selected LRE test data contain order dependence sequences, LSTM is well suited for the estimation of critical measurement parameters. LSTM-based soft sensor (LSTM-SS) was developed and trained using a large sample set of 59,80,000 data points collected over a period of 12 years of LRE ground hot tests. The maximum root mean square error (RMSE) of 6.06% was obtained for propellant flow-rate estimation, whereas the minimum RMSE of 0.22% for chamber pressure and RMSE of 0.87% for turbine speed were obtained. The obtained results show that the designed LSTM-SS has superior performance and better generalization for practical use.