Dynamic cloud back-propagation networks and its application in fault diagnostic for liquid-propellant rocket engines. (March 2018) - Nie, Yao; Cheng, Yuqiang; Wu, Jianjun
Fault diagnosis for liquid-propellant rocket engines often faces a lack of prior knowledge or insufficient sampling data, and thus becomes a decision-making problem with uncertain information sources. In this paper, a method based on dynamic cloud back-propagation networks is proposed. This uses cloud theory to synthetically combine randomness and fuzziness. In this work, a cloud model and back-propagation neural network are synthetically combined in series. A cloud transformation is used to identify the network structure and extract the features of the cloud model. Simultaneously, a unit-delay step is introduced into the input layer to describe the dynamic behaviour during the engine working process. The proposed fault diagnosis method for liquid-propellant rocket engines is verified using the actual data. The results confirm that the proposed method accurately recognizes all three relevant failure modes. Further, randomness associated with the measurement process and ambient noises are simulated by adding random noise to the test conditions. Simulation results demonstrate that the method correctly detects and classifies faults according to the principles of sustainability, indicating a high robustness towards noise. The proposed method has a single-step operating time of 1.24 × 10 −4  s, satisfying the real-time requirements for fault diagnosis in liquid-propellant rocket engines.