Wavelet transformation and deep learning-based rolling bearing weak fault diagnosis method

Abstract

The invention discloses a wavelet transformation and deep learning-based rolling bearing weak fault diagnosis method. The method comprises the following steps: a rolling bearing vibration signal is obtained, and the acquired vibration signal is subjected to continuous wavelet transformation to obtain a time-frequency diagram; autocorrelation operation is performed on wavelet coefficients corresponding to each frequency on the time-frequency diagram to filter out noise interference and extract periodic fault components; the Hilbert transformation is used to perform envelope demodulation to obtain a fault characteristic frequency; a processed time-frequency diagram is input as a feature diagram, and categories of early faults are determined by training a deep learning classification model. Theoretical and experimental results show that fault categories can be determined accurately at an early stage when weak fault of a rolling bearing occur based on an improved wavelet time-frequency diagram used as the classification model for input training, right determination results can be given when the method is applied to different bearings, high training speed can be realized, and high actual application value can be achieved.

Description

technical field [0001] The invention belongs to the field of mechanical equipment signal processing, and in particular relates to a diagnosis method for early weak faults of rolling bearings. Background technique [0002] Rolling bearings are one of the most widely used and most critical parts in rotating machinery. Its operating state often determines the performance of the whole machine. Any slight fault will have a great impact on the operating stability of the equipment, and even cause safety hazards and cause major economic losses. loss. If the weak fault signal of the bearing can be extracted in the early stage of the fault, the signal can be analyzed and processed, and the accurate diagnosis result can be given in time, so that the maintenance personnel can formulate an effective and reasonable maintenance plan for the fault, thereby prolonging the life of the equipment and greatly Reduce the harm caused by failure. Therefore, the research on diagnosis of early weak...

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Problems solved by technology

[0004] However, during the early fault diagnosis of the bearing, especially in the weak fault diagnosis, the local defects and damage of the bearing are very small, and the shock vibration caused by it is very weak. In addition, the discrete intervals of frequency band division methods such as discrete wavelet or wavelet packet transform and empirical mode decomposition are too large and too rough, which will also affect the extraction of fault features, and it is difficult to achieve ideal results in early weak fault diagnosis.

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