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Fault diagnosis method of rolling bearing based on improved multi-scale fuzzy entropy

A kind of rolling bearing and fault diagnosis technology, applied in the direction of mechanical bearing testing, mechanical component testing, character and pattern recognition, etc., can solve the problem of ignoring the overall trend of the signal, and achieve the effect of enriching bearing status information and high recognition rate

Inactive Publication Date: 2019-03-05
UNIV OF SHANGHAI FOR SCI & TECH
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Problems solved by technology

[0003] However, fuzzy entropy subtracts a local mean when constructing the vector needed for calculation, which makes the overall trend of the signal ignored when calculating entropy

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  • Fault diagnosis method of rolling bearing based on improved multi-scale fuzzy entropy
  • Fault diagnosis method of rolling bearing based on improved multi-scale fuzzy entropy
  • Fault diagnosis method of rolling bearing based on improved multi-scale fuzzy entropy

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Embodiment Construction

[0022] In order to overcome the limitation that the traditional fuzzy entropy ignores the overall trend of the signal during calculation, and at the same time, in order to improve the efficiency of fault diagnosis and reduce the interference of human factors on the diagnosis results, the present invention provides an improved multi-scale fuzzy entropy and support vector The rolling bearing fault diagnosis method of the machine, specifically adopts the following technical scheme:

[0023] 1. Improved multi-scale entropy algorithm

[0024] 1.1 Multi-scale fuzzy entropy

[0025] Both approximate entropy and sample entropy are based on the Heaviside function (unit step function) to define the similarity of vectors, which leads to the result of traditional binary classification, while the boundaries of classes in the real world are fuzzy, and it is difficult to directly determine a Whether a pending pattern falls into a class at all. Fuzzy entropy introduces the concept of fuzzy ...

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Abstract

The invention relates to a rolling bearing fault diagnosis method based on improved multi-scale fuzzy entropy, which collects the vibration signal of the rolling bearing; calculates the improved multi-scale fuzzy entropy of the vibration signal; uses the improved fuzzy entropy on the first eight scales as the bearing fault feature vector; The fault feature vector is divided into training set and test set; the training set is used to train the support vector machine and the trained model is used to predict the test set; the working state and fault type of the rolling bearing are identified according to the prediction results. The fuzzy entropy algorithm is improved, and the local mean value in the traditional fuzzy entropy calculation is replaced by an overall mean value to calculate the improved fuzzy entropy at different scales. The improved multi-scale fuzzy entropy can reflect the characteristics of the signal more comprehensively, so as to evaluate the running state of the bearing more accurately. The invention can extract richer bearing state information and has a higher recognition rate in the fault mode recognition process.

Description

technical field [0001] The invention relates to a fault diagnosis technology, in particular to a rolling bearing fault diagnosis method based on improved multi-scale fuzzy entropy. Background technique [0002] Rolling bearings are one of the key components in rotating machinery, and their operating status often determines the performance of the entire machine. Therefore, the fault diagnosis of rolling bearings is of great significance. Among various bearing fault diagnosis methods, the diagnosis based on vibration signal is one of the most commonly used and most effective methods. However, rolling bearings will inevitably be affected by nonlinear factors such as friction, clearance and nonlinear stiffness during operation, and the collected vibration signals often present strong nonlinear and unsteady characteristics. Therefore, the traditional linear system-based time-domain and time-frequency domain signal analysis methods are difficult to accurately extract bearing fau...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/04G06K9/62
CPCG01M13/045G06F18/2411G06F18/2414
Inventor 朱可恒李郝林陈龙景璐璐
Owner UNIV OF SHANGHAI FOR SCI & TECH