Servo systems are widely used in aerospace and other fields. To ensure the safety of the system operation, the fault detection of servo systems is very important. Servo system fault detection often uses comparative differential judgment method, which requires the establishment of a reliable model. On the one hand, servo system models are usually built using mathematical and physical methods by combining the transfer functions of the various components the actual servo system have into an algorithm that calculates the input–output relationship. On the other hand, machine learning algorithms can also be used to model the servo system. In this paper, we address this issue by developing a rocket data prediction model based on the LSTM algorithm using publicly available data provided by Shanghai Aerospace Control Technology Institute. After deriving the results, the results are evaluated by using R-squared metrics and some improvement outlooks are provided for the subsequent research.
Hydraulic Servo System Fault Detection Based on Machine Learning
Lect. Notes Electrical Eng.
International Conference on Aerospace System Science and Engineering ; 2021 July 14, 2021 - July 16, 2021
Proceedings of the International Conference on Aerospace System Science and Engineering 2021 ; Chapter : 15 ; 187-195
2022-07-09
9 pages
Article/Chapter (Book)
Electronic Resource
English
Hydraulic Servo System Fault Detection Based on Machine Learning
British Library Conference Proceedings | 2023
|Fault Detection and Diagnosis of a Linear Hydraulic Servo Axis
British Library Conference Proceedings | 2004
|Model-Based Real-Time Fault Monitoring Strategy for Hydraulic Servo Actuators
Springer Verlag | 2024
|Modeling and Fault Diagnosis of Aviation Hydraulic Servo Actuator
Springer Verlag | 2022
|