A data-driven algorithm-based health monitoring method for space station payloads

Abstract

The invention provides a health monitoring method for effective loads of a space station based on a data-driven algorithm. In the design stage, after historical data of the effective loads are subjected to state vector construction, parameter standardization and weight processing, training samples are obtained; then, clustering learning is performed on the training samples, and different working condition data classifications can be obtained. In the running stage, after real-time downlink test data of the effective loads are processed, through the working conditions obtained through clustering learning, the downlink data are monitored in real time, if abnormal data occur, it shows that new working conditions happen to the loads, a fault may happen or is about to happen probably, finally, the abnormal data are detected in combination with a fault diagnosis tree method, and the position of the fault is determined. Through machine learning of the historical data, a system health knowledge base is formed, the abnormal state of the loads is found through calculation of the distance value of outliers, real-time monitoring on the health state of the loads is achieved, fault detection and positioning of the loads can be supported, and prediction to a certain extent is achieved.

Description

technical field [0001] The invention belongs to the technical field of payload fault diagnosis and health management of a space station application system, and in particular relates to a method for monitoring the payload health of a space station based on a data-driven algorithm. Background technique [0002] Space missions have the characteristics of significant political influence, high risk, large investment, and long cycle. Therefore, ensuring the smooth implementation of space missions is an important goal of the country. [0003] In order to ensure the smooth implementation of space missions, one of the methods usually adopted in the current technology is to design the payload of the space station with a high-reliability design method. However, due to the complexity of the space environment and the limitations of ground test conditions, the space station payload will still fail during operation. Therefore, how to effectively and quickly perform fault prediction and fau...

AI Technical Summary

Problems solved by technology

However, due to the complexity of the space environment and the limitations of ground test conditions, the space station payload will still fail during operation. Therefore, how to effectively and quickly perform fault prediction and fault diagnosis on the space station payload has important practical significance

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