Rolling bearing failure diagnostic method based on multi-characteristic parameter

Abstract

The invention discloses a rolling bearing failure diagnostic method based on a multi-characteristic parameter, which comprises the following steps of: (1) pre-processing the collected vibrating signals, and removing the interference of the noise and other vibrating sources; (2) extracting a time domain statistical parameter capable of reflecting different working conditions of the rolling bearing from the vibrating signals; (3) figuring out the envelope signal of the pre-processed vibrating signals, decomposing the envelope signal through an improved empirical mode decomposition method to obtain a series of intrinsic mode functions; (4) selecting multiple intrinsic mode functions concentrating most part of energy, and calculating an energy torque; (5) performing envelope spectrum analysis on the first decomposed intrinsic mode function, and calculating the failure characteristic amplitude ratio; and (6) serving a plurality of characteristic parameters extracted in the step as input vector of a BP neural network, and outputting the diagnosis result through the network. The rolling bearing failure diagnostic method disclosed by the invention can fully reflect the operation condition of the rolling bearing, improve the diagnosis accuracy and facilitate realization of the online monitoring of the rolling bearing.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a rolling bearing fault diagnosis method based on multiple characteristic parameters. Background technique [0002] Vibration diagnosis is a basic method for complex electromechanical system diagnosis. Vibration testing methods and vibration analysis theory are mature, and it is easy to realize online monitoring and diagnosis. It is currently one of the most widely used methods in rolling bearing fault diagnosis. As the most commonly used analysis tool in stationary signal processing, the traditional Fourier transform is not suitable for nonlinear and non-stationary signals. In order to overcome the limitations of Fourier transform, time-frequency analysis methods have been developed rapidly. [0003] Short-time Fourier transform is the earliest time-frequency analysis method, but it is based on the assumption that the signal is a stationary signal in the time...

AI Technical Summary

Problems solved by technology

At present, the artificial neural network has been widely used in the fault diagnosis of rolling bearings. Although good diagnostic results have been achieved, most of them use a single characteristic parameter as the input vector of the neural network, which cannot fully reflect the fault status of rolling bearings. Moreover, the accuracy of diagnosis needs to be further improved

In addition, for the extraction algorithm of rolling bearing characteristic parameters, the current commonly used method is to extract the energy or energy entropy of the decomposed signal after the vibration signal is transformed by wavelet or wavelet packet, but for nonlinear and unsteady rolling bearing vibration signals, the energy or Energy entropy cannot better reflect the nature of the signal

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