Variational Nonlinear Mode Decomposition Method for Fault Diagnosis of Variable Speed ​​Bearings

Abstract

The invention discloses a method for fault diagnosis of a variable-rotating-speed bearing through variation nonlinear mode decomposition. The method for fault diagnosis of the variable-rotating-speedbearing through variation nonlinear mode decomposition comprises the steps that a dynamic signal of a rolling bearing is collected by a vibration signal sensor; a low-frequency region is separated outfrom the vibration signal, a resonance band is recognized through a rapid spectrum kurtosis method, and thus frequency band separation is achieved; a rotating frequency curve is extracted from the low-frequency region and a fault feature frequency curve is extracted from the resonance band through a ridge extraction algorithm, and rough frequency information is obtained; with the extracted roughfrequency information as an initial value, optimization analysis is carried out through a variation nonlinear mode decomposition method, and the accurately estimated rotating frequency and the accurately estimated fault feature frequency are obtained; and feature order calculation is carried out on the optimized rotating frequency and fault feature frequency, and fault types are judged by comparison with theoretical values.

Description

technical field [0001] The invention relates to the field of bearing diagnosis, in particular to a method for diagnosing faults of variable speed bearings by decomposition of a variational nonlinear mode. Background technique [0002] Rotating machinery equipment is developing towards large-scale, precision and intelligentization. The state of mechanical parts directly affects the operation status and safety status of mechanical equipment, and rolling bearings are one of the most commonly used parts in various types of rotating machinery. one. Therefore, it is extremely important to detect the health status of rolling bearings. When a bearing component fails, there will be a periodic transient impulse response, how to effectively extract and accurately evaluate it is the key to bearing fault diagnosis. However, due to the complexity of the working environment, the vibration signals collected from the equipment site are often variable speed and load, and the signal has nonl...

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Problems solved by technology

It should be noted that in the actual time-frequency analysis, the time-frequency diagram of the traditional method has poor aggregation, complex calculation, and poor timeliness, so the frequency information extracted by the ridge line is not accurate enough

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