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Suspension system early fault detection method based on random matrix theory and cumulative sum

A random matrix theory and early failure technology, applied in computer-aided design, complex mathematical operations, geometric CAD, etc., can solve the problems of suspension system health reduction and performance failure

Active Publication Date: 2020-06-09
NAT UNIV OF DEFENSE TECH +1
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Problems solved by technology

However, the health of the suspension system gradually reduces its performance during its working life until it fails

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  • Suspension system early fault detection method based on random matrix theory and cumulative sum
  • Suspension system early fault detection method based on random matrix theory and cumulative sum
  • Suspension system early fault detection method based on random matrix theory and cumulative sum

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Embodiment Construction

[0050] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0051] For better understanding of the present invention, make following explanation in advance:

[0052] figure 1 The flow chart of the early fault detection method of the suspension system based on random matrix theory and cumulative sum provided by the embodiment of the present invention. Such as figure 1 As shown, the early fault detection method of suspension system based on random matrix theory and cumulative sum includes the following steps:

[0053] S1. Construct an initial matrix based on random matrix theory and big data;

[0054]S2. Convert the initial matrix into a batch random matrix through a preset moving window;

[0055] S3, according to random matrix theory single ring theor...

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Abstract

The invention discloses a suspension system early fault detection method based on a random matrix theory and cumulative sum. The method comprises the following steps: S1, constructing an initial matrix based on the random matrix theory and big data; S2, converting the initial matrix into a batch random matrix through a preset moving window; S3, converting the batch random matrix into a non-Hermitematrix according to a random matrix theory monocyclic theorem; S4, calculating a characteristic value of the non-Hermite matrix, obtaining an MSR of the characteristic value according to the characteristic value, and taking the MSR as a health state value of the suspension system; and S5, realizing early fault detection of the suspension system through a cumulative sum function according to the obtained health state value of the suspension system. According to the method, the health state value of the suspension system is obtained through the random matrix theory, early fault detection is achieved through the historical value of the health state of the suspension system and the cumulative sum function, and the method has the advantages of being simple in detection and high in reliability.

Description

technical field [0001] The invention relates to the technical field of maglev trains, in particular to an early fault detection method of a levitation system based on random matrix theory and cumulative sum. Background technique [0002] With the popularization of maglev trains, the safety and reliability of the levitation system has attracted more and more attention. During the operation of the maglev train, once the suspension system fails, the train will not be able to operate. If the early failure of the suspension system can be detected before the suspension system failure occurs, such things will be avoided to a large extent. However, the health of the suspension system gradually reduces its performance during its working life until it fails. Its degradation process is usually affected by two kinds of changes, namely individual variability and temporal variability. Therefore, how to accurately detect the early failure of the suspension system is an urgent problem to...

Claims

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Application Information

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IPC IPC(8): G06F30/15G06F30/20G06F17/16G06F111/10G06F119/04
CPCG06F17/16
Inventor 龙志强王平李晓龙苗欣江守亮刘纪龙
Owner NAT UNIV OF DEFENSE TECH
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