Fault diagnosis method of motor bearing based on time-frequency domain statistical characteristic

A motor bearing and statistical feature technology, applied in the field of motor bearing fault diagnosis based on time-frequency domain statistical features, can solve problems such as low fault diagnosis accuracy and poor signal extraction effect, improve accuracy and reliability, and reduce misdiagnosis and missed diagnosis rate, highlighting the effect of fault information

Active Publication Date: 2019-06-14
XI AN JIAOTONG UNIV
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Problems solved by technology

[0005] Aiming at the problems existing in the prior art, the present invention proposes a motor bearing fault diagnosis method based on time-frequency domain statistical characteristics, which can well solve the problems of poo

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  • Fault diagnosis method of motor bearing based on time-frequency domain statistical characteristic
  • Fault diagnosis method of motor bearing based on time-frequency domain statistical characteristic
  • Fault diagnosis method of motor bearing based on time-frequency domain statistical characteristic

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Embodiment Construction

[0055] The following will refer to the attached Figure 1 to Figure 7 Specific examples of the present invention are described in more detail. Although specific embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and is not limited to the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0056] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art should understand that they may use different terms to refer to the same component. The specification and claims do not use differences in nouns as a way of distinguishing components, but use differences in functions of components as a criterion for distinguishing. "Includes" or "comprises" mentioned throughout ...

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Abstract

The invention discloses a fault diagnosis method of a motor bearing based on a time-frequency domain statistical characteristic. The fault diagnosis method comprises the following steps that: speed pulse signals and vibration signals of the motor bearing in T time interval are collected simultaneously, and a frequency conversion curve f is calculated according to the speed pulse signals; the vibration signals at a stationary speed are intercepted according to the frequency conversion curve and are sectioned; health indicators of the vibration signals in all segments are calculated according toa formula of the health indicators, a fault degree grade of each segment is obtained by comparing the health indicators with a fault threshold table, and the fault grade with the highest fault degreegrades counted is the fault grade of the motor bearing; Hilbert envelope spectrums of the vibration signals in all segments are calculated, effective frequencies of all envelope spectrums are determined according to a calculation method of the effective frequencies, the most frequent occurrence frequency of the effective frequencies counted is a fault frequency, and then a fault position of the bearing is determined. The overall fault degree and the fault position of the motor bearing are accurately obtained by counting the fault grade of the vibration signals and the effective frequencies ofthe envelope spectrums in a plurality of time intervals.

Description

technical field [0001] The invention belongs to the technical field of signal processing analysis and fault diagnosis, in particular to a motor bearing fault diagnosis method based on time-frequency domain statistical characteristics. Background technique [0002] The working state of the motor bearing directly affects the operation safety and performance of the entire equipment. At present, there are many types of testing equipment and testing methods for the motor bearing, usually based on the relevant standards of the bearing industry, under a certain speed and test load, by measuring the bearing. Information such as vibration acceleration, temperature, and acoustic emission can be used to monitor, vibrate, and alarm the motor based on time-frequency domain characteristics. [0003] However, the existing fault diagnosis methods for motor bearings still have the problem of low fault diagnosis accuracy. The impact signals caused by bearing faults are usually mixed in the ba...

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Application Information

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IPC IPC(8): G01M13/045
Inventor 刘一龙陈雪峰张兴武白晓博张启旸张子泷
Owner XI AN JIAOTONG UNIV
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