Low-speed heavy-duty bearing fault identification method and system, medium, equipment and terminal

Abstract

The invention belongs to the technical field of rolling bearing fault recognition, and discloses a low-speed heavy-duty bearing fault recognition method and system, a medium, equipment and a terminal, and the method comprises the steps: carrying out the filtering decomposition of a signal, solving the feature quantities of the first three component signals obtained through the decomposition, and constructing a feature value matrix; carrying out dimensionality reduction on the characteristic value matrix by adopting a distance evaluation technology, and screening out significant characteristics; and inputting the significant features into a BP neural network for training and testing, thereby realizing fault identification of the low-speed heavy-duty bearing. Aiming at the problems of broadband, non-stability and strong noise of an original signal, the method focuses on analyzing the step of filtering decomposition of the signal, solves the characteristic quantities of the first three component signals obtained by decomposition and constructs a characteristic value matrix, adopts a distance evaluation technology method to carry out dimension reduction on the characteristic value matrix, screens out significant characteristics, and finally obtains the characteristic value matrix. And inputting the significant features into a BP neural network for training and testing, thereby realizing accurate identification of the fault type of the low-speed heavy-duty bearing.

Description

technical field [0001] The invention belongs to the technical field of rolling bearing fault identification, and in particular relates to a low-speed and heavy-load bearing fault identification method, system, medium, equipment and terminal. Background technique [0002] At present, rolling bearings are the main components to maintain the normal operation of mechanical equipment. Ensuring that it is in good condition plays a key role in the normal operation of the equipment. In most cases, the failure of mechanical equipment is closely related to rolling bearings. Therefore, the fault diagnosis of rolling bearings is of great significance to engineering safety and is an important research content in the field of mechanical fault diagnosis. Once the rolling bearing fails, it will often cause a chain reaction, resulting in varying degrees of damage to the overall mechanical equipment. In severe cases, it may cause equipment downtime or even cause an accident. Therefore, durin...

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Problems solved by technology

[0005] (1) The traditional vibration test-based rolling bearing diagnosis technology often assumes that the working condition is constant speed and constant load, and assumes that the fault only comes from equipment degradation or failure. It is not suitable for complex working conditions with low speed and alternating heavy load, and has limited applicability The problem

[0006] (2) In the field of engineering, the commonly used time-frequency analysis method is used to extract fault features from noise signals, but it is prone to frequency ambiguity and mode aliasing, which leads to errors in signal decomposition results and affects the accuracy of fault feature extraction

[0007] (3) Existing fault feature extraction methods are mostly used to extract narrowband signal components, which are prone to corner sharpening due to the influence of extreme point interpolation functions or the Gibson effect, resulting in a series of fluctuations in the extraction results

Therefore, it is difficult to accurately extract the broadband fault feature information of the signal from the non-stationary and strong noise

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