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Rolling bearing state automatic early warning method based on extraction and selection of multiple characteristics

A rolling bearing and feature extraction technology, which is applied in the field of automatic early warning of rolling bearing status, can solve the problems of inability to intelligently construct the bearing recession mode and no automatic alarm strategy.

Inactive Publication Date: 2014-06-18
UNIT 63680 OF PLA
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  • Abstract
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Problems solved by technology

However, this method only artificially specifies a few features for fusion, and cannot intelligently construct a feature subset that can stably reflect the bearing degradation mode from many original features, and does not formulate a corresponding automatic alarm strategy

Method used

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  • Rolling bearing state automatic early warning method based on extraction and selection of multiple characteristics
  • Rolling bearing state automatic early warning method based on extraction and selection of multiple characteristics
  • Rolling bearing state automatic early warning method based on extraction and selection of multiple characteristics

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Embodiment Construction

[0044] Such as figure 1 In the process shown in the present invention, the effectiveness of the present invention is verified by taking the vibration signal of the rolling bearing in the accelerated fatigue test of the azimuth axis transmission mechanism of the ship-borne satellite communication antenna as an example. The azimuth shaft transmission mechanism of the shipborne satellite communication antenna is mainly composed of a motor, a planetary reducer and a gearbox. The gearbox contains 4 rolling bearings, which are respectively located at the upper and lower ends of the input shaft and the output shaft. During the experiment, the rotational speed of the motor was 1500rpm, and the radial vibration acceleration signal at each bearing seat of the gearbox was collected by using the PCB333B32 vibration acceleration sensor and the ECON AVANT integrated data acquisition and analyzer. The sampling frequency was 20KHz, and the number of sampling points was 20480. Vibration data w...

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Abstract

The invention discloses a rolling bearing state automatic early warning method based on extraction and selection of multiple characteristics. The method comprises the steps of extracting multiple time domain, frequency domain and time-frequency domain characteristics of a vibration signal of a rolling bearing, and intelligently selecting a characteristics subset which is sensitive to the fatigue recession process of the rolling bearing and can supply complementary information by using a supervision characteristic-free selection method based on maximum correlation and minimum redundancy. The blindness of manually selecting sensitive characteristics without priori knowledge is overcome, an alarm policy is set up according to an alarm threshold value automatic establishing method, and the early damage warning of the rolling bearing is realized.

Description

technical field [0001] The invention relates to the field of mechanical fault diagnosis, in particular to an automatic early warning method for a rolling bearing state. Background technique [0002] The state evaluation technology of rolling bearings based on vibration signals generally directly selects a single feature of the vibration signal as the state evaluation index. However, with further research, it is found that a single feature is usually only effective for a certain defect at a certain stage, the same feature behaves differently under different failure modes, different features behave differently under the same working conditions, and effective state evaluation indicators It should be able to make full use of a variety of information, which can not only capture the inherent performance changes of bearings in different operating stages, but also be easy to obtain in practical applications. In order to improve the performance of a single indicator, some scholars h...

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Application Information

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IPC IPC(8): G01M13/04
Inventor 李康陈雪军刘冰胡湘江林习良訾艳阳蔡自刚
Owner UNIT 63680 OF PLA
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