Industrial system fault detection method based on Euclidean distance multiscale fuzzy sample entropy

A Euclidean distance, industrial system technology, applied in the research field of system complexity, to achieve the effect of improving accuracy and stability, overcoming one-sidedness, and increasing inaccuracy and instability

Active Publication Date: 2020-05-08
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies in the above-mentioned traditional method for describing the complexity of the time series of industrial system signals, and proposes an industrial system fault detection based on Euclidean distance based Multiscale Fuzzy Entropy (EuclideanDistance basedMultiscale Fuzzy Entropy, EDM-Fuzzy) technology

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  • Industrial system fault detection method based on Euclidean distance multiscale fuzzy sample entropy
  • Industrial system fault detection method based on Euclidean distance multiscale fuzzy sample entropy
  • Industrial system fault detection method based on Euclidean distance multiscale fuzzy sample entropy

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[0045] The present invention will be further described in detail below in conjunction with the accompanying drawings and implementation methods.

[0046] refer to figure 1 Execute steps to illustrate the implementation process of the present invention on bearing fault diagnosis:

[0047] An industrial system fault detection method based on Euclidean distance multi-scale fuzzy sample entropy, including the following steps:

[0048] Step 1. Collect the original vibration signal time series (about 48,000 data points in length) under different state types of the bearing through the bearing vibration signal acquisition equipment, and divide it into a set of sub-sequences of 2,000 points (a set of about 240 subsequence);

[0049] The bearing vibration signal acquisition equipment includes a 2 horsepower motor, a torque sensor, a power meter and an electronic control device.

[0050] The collected bearing status types are divided into six types, which are normal status, ball beari...

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Abstract

The invention discloses an industrial system fault detection method based on Euclidean distance multiscale fuzzy sample entropy. According to the method provided by the invention, the complexity of atime sequence can be described from a plurality of time scales; meanwhile, compared with an existing multiscale entropy method and an existing composite multiscale entropy (FME) method, the method hasthe advantages that the stability and the accuracy of the calculation of multi-scale fuzzy sample entropy (FME) are remarkably improved. The method can be used for judging and detecting the fault type of an industrial system and analyzing the complexity of the time sequence.

Description

technical field [0001] The invention relates to the research field of system complexity, and relates to a method for describing the time series complexity of industrial system signals, in particular to an industrial system fault detection method based on Euclidean distance multi-scale fuzzy sample entropy. Background technique [0002] The time series of bearing vibration signals is an important high-dimensional data type, which is a sequence composed of the sampling values ​​of a certain physical quantity of an objective object at different time points in chronological order. Quantitatively analyzing the complexity of a signal time series is a complex and important task for understanding the operation rules of a system. In order to analyze the characteristics of time series and distinguish the normal and chaotic behavior of the system, many years ago experts and scholars proposed many methods to measure the complexity of a system signal. [0003] Multiscale Entropy (Multis...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/045G06N3/04
CPCG01M13/045G06N3/04
Inventor 周仁杰王晓万健张纪林张伟蒋从锋
Owner HANGZHOU DIANZI UNIV
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