Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

On-orbit spacecraft flywheel fault detection method based on kernel principal component analysis

A nuclear principal component analysis and fault detection technology, which is applied in the direction of instruments, electrical testing/monitoring, testing/monitoring control systems, etc., can solve the lack of independent research and judgment of unknown faults, the lack of online analysis capabilities of massive data, and the deep extraction of fault data Insufficient and other problems, to achieve the effect of intuitive display of detection indicators, improved accuracy, and strong scalability

Active Publication Date: 2021-08-27
BEIHANG UNIV
View PDF4 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In summary, there are four deficiencies in the existing fault diagnosis methods. First, the ability to independently study and judge unknown faults is insufficient.
Second, the online analysis ability of massive data is insufficient
Third, the ability to deeply refine fault data is insufficient
Fourth, the ability to accurately identify complex faults is insufficient

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • On-orbit spacecraft flywheel fault detection method based on kernel principal component analysis
  • On-orbit spacecraft flywheel fault detection method based on kernel principal component analysis
  • On-orbit spacecraft flywheel fault detection method based on kernel principal component analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0103] The implementation process of the present invention will be described in detail below by taking a certain type of spacecraft that uses three orthogonal flywheel sets as the actuator as an example.

[0104] The flywheel fault modeling formula is:

[0105] T m (t f ) = e mf T mt (t f )+f a (twenty one)

[0106] t f is the fault time, T m (t f ) is the actual electromagnetic torque generated by the flywheel motor at this time, e mf ∈[0,1] is the current multiplicative effective coefficient of the flywheel, T mt (t f ) for t f The electromagnetic torque that the flywheel motor should produce under normal conditions at time, f a is its current additive bias.

[0107] The common gain fault, deviation fault and idling fault of the flywheel are selected for simulation, and the faults are correspondingly set on the flywheel 1, 2, and 3. The specific fault parameters are shown in Table 1:

[0108] Table 1 Flywheel failure parameters

[0109]

[0110] 1, the ap...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an on-orbit spacecraft flywheel fault detection method based on kernel principal component analysis. The method comprises the steps of correspondingly collecting the real-time state information of the rotating speed, torque and the like of a flywheel through a sensor according to different control modes and detection modes of the flywheel, carrying out noise reduction through a wavelet threshold method, and carrying out the homologous dimension expansion; carrying out high-dimensional mapping on the data through a kernel principal component analysis method, obtaining T2 and SPE control limits reflecting the degree of deviation from a training model according to historical normal data, and then obtaining a comprehensive index fault control limit; and calculating T2 and SPE statistics of the real-time data, then obtaining a comprehensive index, and judging whether a fault exists or not according to whether the comprehensive index exceeds a limit or not. According to the invention, high accuracy can be achieved for the single-machine fault and the multi-machine fault of the component-level flywheel, and the fault flywheel can be positioned; the method is more visual in detection index display, and engineering practice is utilized; and the method is high in expandability and has a relatively great application prospect.

Description

【Technical field】 [0001] The present invention provides a flywheel fault detection method based on nuclear principal component analysis for low-earth orbit spacecraft when performing inertial stabilization tasks, and the invention belongs to the field of fault detection of spacecraft actuators. 【Background technique】 [0002] Spacecraft accidents have been common since humans entered space. According to statistics, from 1990 to 2001, 121 of the 764 spacecraft launched by countries such as the United States, Europe, Japan, and Canada failed; during the period from 1990 to 2006, the economic losses caused by the failure of commercial satellites and scientific satellites were as high as 4.4 billion. Dollar. In recent years, control subsystem failures account for about 30% of the total number of failures. Typical cases include the U.S. GPS BII-7 satellite in 1996 due to attitude reaction flywheel failure, the satellite was completely out of control; in 2001, the BeppoSAX satel...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G05B23/02
CPCG05B23/0221
Inventor 聂小辉金磊
Owner BEIHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products