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Spacecraft fault detection method based on Riemannian measurement

A fault detection and spacecraft technology, applied in the direction of instruments, electrical testing/monitoring, testing/monitoring control systems, etc., can solve problems such as difficulty in obtaining probability density functions, non-constant data mean and variance, complex fault types, etc., to achieve applicable broad effect

Active Publication Date: 2021-10-12
BEIJING INST OF SPACECRAFT SYST ENG
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Problems solved by technology

[0007] The technical problem solved by the present invention is: in order to solve the problems of non-constant mean value and variance of sampling data in complex industrial processes, difficult acquisition of probability density function, and complex fault types, a spacecraft fault detection method based on Riemann metric is proposed

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  • Spacecraft fault detection method based on Riemannian measurement
  • Spacecraft fault detection method based on Riemannian measurement
  • Spacecraft fault detection method based on Riemannian measurement

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specific Embodiment approach 2

[0044] Specific embodiment two: This embodiment is to further illustrate the fault diagnosis algorithm based on the Riemann metric described in the specific embodiment one. In this embodiment, due to the Riemann center P of a plurality of positive definite matrices in step two g The solution is more difficult, and the following iterative solution method is given:

[0045] Table 1 Riemann center iterative solution algorithm

[0046]

specific Embodiment approach 3

[0047] Specific embodiment three: this embodiment is to further explain the fault diagnosis algorithm based on the Riemann metric described in specific embodiment one, in this embodiment, such as image 3 As shown, the specific steps of the threshold setting algorithm in step three are as follows:

[0048] According to the formula of the monitoring index J, the offline positive definite matrix set P i with the Riemann Center Pg The monitoring index between J i (i=1,...,N) can be written as Order N 0 satisfy

[0049]

[0050] where α is the acceptable false alarm rate (FAR).

[0051] Table 2 Threshold setting algorithm

[0052]

[0053] The invention proposes a spacecraft off-line process data set establishment and Riemann center calculation method under the condition of no failure. Using the historical offline data set, process it into a set of positive definite matrices that can be used for the calculation of Riemann metrics; use the set of positive definite matr...

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Abstract

The invention relates to a spacecraft fault detection method based on Riemannian measurement, and belongs to the technical field of fault diagnosis. The method comprises the steps of 1, collecting N groups of off-line process data {Y1,..., YN} under the fault-free condition, and establishing a positive definite matrix rho (n) = [P1,..., PN]; 2, calculating a Riemannian center Pg of the positive definite matrix [P1,..., PN] through an iterative solution method; 3, sequentially calculating performance indexes Ji corresponding to the offline process data, and calculating a threshold value Jth through a threshold value setting algorithm; and 4, obtaining an online sample value, calculating a performance index J of the online sample value, comparing J with the threshold value Jth obtained in the step 3, if J is greater than or equal to Jth, giving a fault alarm, and if J is less than Jth, determining that no fault exists. According to the method, the fault is detected based on the batch data matrix, the matrix covers the mean value, the covariance and the uncertain information, and the uncertainty can be effectively processed by adopting the Riemannian center.

Description

technical field [0001] The invention relates to a spacecraft fault detection method based on the Riemann metric, and belongs to the technical field of fault diagnosis. Background technique [0002] With the continuous scale and complexity of modern industry, real-time fault detection technology plays an important role in industrial process safety and product quality control. The commonly used fault detection algorithms are divided into the following three categories: model-based methods, knowledge-based methods and data-based methods. Due to the application of distributed systems in modern industry, rich process data are stored in industrial databases. Compared with other methods, data-based methods have greater advantages. [0003] Most data-based fault diagnosis systems are aimed at static systems, mainly including principal component analysis (principle component analysis, PCA), partial least squares (partial least squares, PLS), independent component analysis (independe...

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/024
Inventor 刘一帆常进闫金栋韩小军皇甫松涛李乃海白少华张淳
Owner BEIJING INST OF SPACECRAFT SYST ENG
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