Blind deconvolution algorithm for enhancing rotating machinery fault signal features
A blind deconvolution and fault signal technology, applied in the testing of mechanical components, testing of machine/structural components, complex mathematical operations, etc., can solve the problems of low precision and small scope of application, and achieve improved precision and accuracy , to meet the flexibility requirements, the effect of performance improvement
Pending Publication Date: 2021-10-22
SOUTHEAST UNIV
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[0005] Aiming at the defects of the prior art, the present invention provides a blind deconvolution algorithm that enhances the characteristics of f
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[0037] The specific embodiments of the present invention will be described below in conjunction with the accompanying drawings.
[0038] A blind deconvolution algorithm for enhancing the fault characteristics of rotating machinery signals in this application, please refer to figure 1 , including the following steps:
[0039] S1: Construct multiple cascaded FIR filters, determine the maximization criterion of the blind deconvolution algorithm according to the characteristics of the original vibration signal x, and use the maximization criterion as the objective function J;
[0040] Specifically include:
[0041] S11: collect the original vibration signal x of the rotating machinery through the acceleration sensor as the original input of the blind deconvolution algorithm,
[0042] x=[x 1 ,x 2 ,...,x N-1 ,x N ] (1)
[0043] where x i Represents each data value of the original vibration signal, and N represents the length of the signal.
[0044] S12: Initialize the filte...
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The invention relates to a blind deconvolution algorithm for enhancing rotating machinery fault signal features, and the algorithm comprises the following steps: S1, constructing a plurality of cascaded FIR filters, determining the maximization criterion of the blind deconvolution algorithm according to the features of original vibration signals, and taking the maximization criterion as a target function; S2, performing convolution operation on the original vibration signals in sequence by using a cascaded FIR filter to obtain filtered signals, and calculating an objective function value of the filtered signals; S3, calculating the gradient of the target function value to the filter under the current iteration number by adopting a backward automatic differential algorithm; S4, updating the values of all filters; and S5, repeating the steps S2-S4, and outputting a final filtered signal after the maximum number of iterations is reached. The technical problem that the rotating machine fault diagnosis precision is not high due to the fact that iteration algorithms of different blind deconvolution algorithms cannot be universal and the blind deconvolution algorithms are poor in performance is solved.
Description
technical field [0001] The invention relates to the technical field of fault diagnosis calculation of rotating machinery based on deep learning, in particular to a blind deconvolution algorithm for enhancing the characteristics of fault signals of rotating machinery. Background technique [0002] Gearboxes and bearings are critical components of rotating machinery, and their failure is the most common cause of mechanical failure. In order to reduce potential safety hazards and ensure the normal operation of equipment, it is particularly important to monitor the condition of components such as gearboxes and bearings of rotating machinery. After a gearbox or bearing fault develops, a transient pulse will be generated that will be periodic. However, due to the influence of the transmission path and environmental noise, the fault pulse of the signal collected by the vibration sensor is greatly attenuated, which brings great obstacles to the fault analysis. [0003] Blind decon...
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Login to View More IPC IPC(8): G06F17/16G06N3/04G01M13/045
CPCG06F17/16G01M13/045G06N3/045
Inventor 胡建中方波许飞云贾民平
Owner SOUTHEAST UNIV