Rolling bearing fault intelligent diagnosis method based on improved S-transformation and deep learning

Abstract

The invention discloses a rolling bearing fault intelligent diagnosis method based on improved S-transformation and deep learning. According to the method, a window width adjustment factor is added toS-transformation, so that the window width of a Gaussian window function can be changed, and therefore, the time-frequency resolution of the S-transformation can be improved, and the S-transformationcan accurately detect an impact component in vibration signals, and thus, the fault characteristics of the vibration signals of a rolling bearing can be better extracted. Improved S-transformation isperformed on the vibration signals of the fault of the bearing, so that the feature matrix of the signals can be obtained; the feature matrix is subjected to column-based expansion so as be convertedinto a feature vector, and the feature vector is inputted into a sparse autoencoder model; on the basis of the characteristics of the encoder, the deep features of the data are further extracted, sothat some important hidden information that is not recognized by humans can be mined; the extracted features are accurately classified. With the method of the invention adopted, the accuracy of the fault diagnosis of the rolling bearing can be effectively improved.

Description

Technical field [0001] The invention relates to the technical fields of rolling bearing fault diagnosis and computer artificial intelligence, in particular to a rolling bearing fault diagnosis method based on improved S transformation and deep learning. Background technique [0002] Rolling bearings are the key basic parts and important rotating parts of the equipment manufacturing industry, and are called mechanical joints. It has the advantages of high efficiency, low friction resistance, easy lubrication, etc., and it is widely used in rotating machinery. However, rolling bearings are also one of the most vulnerable components in rotating machinery. According to statistics, they account for a high proportion of all types of failures, about 30%. This is because the rolling bearing is the component with the worst working conditions in the mechanical equipment. It plays a role in bearing the load and transferring the load in the mechanical equipment. Its operating state directly...

AI Technical Summary

Problems solved by technology

Traditional signal processing methods such as Fourier transform, etc., cannot well adapt to non-stationary signals such as bearing vibration signals.

With the continuous improvement of production technology, industrial production has also entered the era of big data. In the face of huge data scale, traditional manual feature extraction methods cannot be well applied. Therefore, there is an urgent need for an intelligent, continuous fault diagnosis A smart way to handle big data emerges

Images