Symbolic dynamics and cloud model based satellite momentum wheel fault detection method

A satellite momentum wheel and dynamic model technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of long signal period, large amount of historical data, and inability to detect early faults, and achieve small differences , Strong anti-noise ability

Active Publication Date: 2016-01-20
HARBIN INST OF TECH +1
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  • Application Information

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Problems solved by technology

The modeling methods in the prior art mainly adopt the model-based modeling method, which ignores the performance degradation process of the momentum wheel and the problems of external environmental interference; in addition, the amount of historical data for the observed variables of the momentum wheel is large, and the signal The cycle is long, so that the characteristic signal cannot be extracted quickly, and the early fault cannot be detected effectively and timely

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  • Symbolic dynamics and cloud model based satellite momentum wheel fault detection method
  • Symbolic dynamics and cloud model based satellite momentum wheel fault detection method
  • Symbolic dynamics and cloud model based satellite momentum wheel fault detection method

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specific Embodiment approach 1

[0031] Specific embodiment one: The satellite momentum wheel fault detection method based on symbolic dynamics and cloud model of this embodiment is realized according to the following steps:

[0032] Step 1. Based on the symbolic dynamics model, select the key univariate to analyze the running state of the momentum wheel, and obtain the cloud model of the momentum wheel;

[0033] Step 2. Analyze the current signal through the cloud model of the momentum wheel, and establish a cloud model to detect the fault of the satellite momentum wheel:

[0034] In each time course, the corresponding current signal information entropy can be calculated through symbolic dynamics, a set of current signal training samples can obtain a set of current information entropy values, and the threshold value of current entropy value samples calculated according to the current information entropy value is the healthy cloud The formula for the radius of the model is as follows:

[0035] h threshold =...

specific Embodiment approach 2

[0052] Embodiment 2: This embodiment is different from Embodiment 1. It is characterized in that Step 1 is based on a symbolic dynamics model, and the key univariate analysis of the operating state of the momentum wheel is selected such as figure 1 Proceed as follows:

[0053] The momentum wheel adopts a DC brushless motor, and there are three nonlinear variables in the momentum wheel. The mathematical model of the state space momentum wheel in the open-loop system is expressed as:

[0054] I · m ω · = G d ω d [ ...

specific Embodiment approach 3

[0059] Specific embodiment three: this embodiment is different from specific embodiment one or two, and is characterized in that based on the symbolic dynamics model, selects the key univariate analysis momentum wheel running state, obtains the cloud model of the momentum wheel as figure 2 Specific steps are as follows:

[0060] Step 11. Reasonably divide the current signal samples;

[0061] Step 12, select the number of characters according to the information entropy value;

[0062] Step 13, calculating the state transition matrix of the current signal;

[0063] Step 14: Extract information entropy according to the state transition matrix.

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Abstract

The present invention discloses a symbolic dynamics and cloud model based satellite momentum wheel fault detection method, which belongs to the field of satellite momentum wheel fault detection. The present invention, in order to solve the problem that an univariate threshold detection method cannot detect an early fault of a momentum wheel system in the prior art, provides the symbolic dynamics and cloud model based satellite momentum wheel fault detection method. The method specifically comprises: establishing a symbolic dynamics model; extracting a current signal entropy value of the momentum wheel; establishing a healthy cloud model of the momentum wheel in a normal operation mode; and detecting an early fault of the momentum wheel according to the healthy cloud model. The satellite momentum wheel fault detection method provided by the present invention can utilize a single variable to detect the early fault of the momentum wheel and is applied to fault detection of the satellite momentum wheel.

Description

technical field [0001] The invention relates to the field of satellite momentum wheel fault diagnosis, in particular to a satellite momentum wheel fault detection method based on symbolic dynamics and cloud models. Background technique [0002] Satellites work in harsh environments such as weightlessness and high and low temperature for a long time. Early failures are easy to deteriorate and develop into serious failures. Momentum wheels are important actuators for satellites. Due to the harsh space environment and frequent use of it, abnormal situations such as weak faults or performance degradation will inevitably occur. Research on early fault detection will help prevent serious faults and reduce the cost of system operation and maintenance. [0003] In the prior art, there are mainly two methods for fault detection of satellite momentum wheels: one is the model-based method. According to the mathematical model of the momentum wheel, an observer-based fault detection met...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 王日新杨天社李玉庆王小乐龚学兵赵静徐敏强
Owner HARBIN INST OF TECH
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