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Planet wheel fault recognition method based on sensitive measure point data fusion

A technology for fault identification and measurement point data, applied in fuzzy logic-based systems, neural learning methods, biological neural network models, etc. Overcome the effect of inconsistencies in fault susceptibility

Inactive Publication Date: 2012-07-18
XI AN JIAOTONG UNIV
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Problems solved by technology

However, the classification algorithm based on ANFIS depends on the extracted characteristic parameters and the sensitivity of the measured data to faults, the accuracy and stability are not very high, and further improvement is needed

Method used

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  • Planet wheel fault recognition method based on sensitive measure point data fusion
  • Planet wheel fault recognition method based on sensitive measure point data fusion
  • Planet wheel fault recognition method based on sensitive measure point data fusion

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Embodiment Construction

[0017] The present invention will be described in further detail below in conjunction with accompanying drawing: figure 1 Shown, flow process of the present invention is as follows:

[0018] 1) Select three measuring points on the planetary gearbox, two measuring points at the input end are selected to be arranged at 90° on the outside of the end cover, respectively measure the vibration in the vertical radial direction and the horizontal radial direction, and one measuring point is selected at the output end , to measure the vibration signal in the vertical direction;

[0019] 2) Use the historical data to calculate the two characteristic parameters of the effective value of the filtered signal (FRMS) and the normalized positive difference spectrum (NSDS), and form a feature set as the input of the classification algorithm based on ANFIS to train the classification algorithm, in which the historical data The fault category is equal to the classification number of the classif...

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Abstract

The invention discloses a planet wheel fault recognition method based on sensitive measure point data fusion, which includes: selecting characteristic parameters strong in applicability of planetary gear box fault recognition, training a sorting algorithm based on an adaptive neural-fuzzy inference system (ANFIS) by aid of historical data, and adopting the trained sorting algorithm to achieve automatic sorting recognition of planetary gear box faults on the basis of vibration data measured at a plurality of measure points. The planet wheel fault recognition method has the advantages of fusing information of the plurality of measure points, performing fault sorting by aid of the sorting algorithm based on the ANFIS and achieving accurate recognition of planetary gear box faults, solves the problem that different measure point information is inconsistent in fault sensitivity degree, and remarkably improves accuracy and stability of fault recognition.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of mechanical equipment, and relates to a planetary gear fault identification method based on fusion of sensitive measuring point data. The method can accurately classify and locate different fault modes of planetary gear boxes, and realize effective identification of planetary gear box faults . Background technique [0002] The planetary gearbox is generally composed of three parts: the sun gear, the planetary gear and the inner ring gear. It is widely used in mechanical transmission systems in different industries because of its small size, large transmission ratio, strong load capacity, and high transmission efficiency. In a harsh working environment, once a component of the planetary gearbox fails, it may trigger a chain reaction, causing the entire transmission system to fail to operate normally, causing huge economic losses and even casualties. Therefore, the accurate diagnosis of planetary g...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06N7/02
Inventor 雷亚国林京韩冬孔德同廖与禾王琇峰
Owner XI AN JIAOTONG UNIV
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