Method and device for extracting weak characteristic from fault signal of antifriction bearing

Abstract

The invention discloses a method and device for extracting a weak characteristic from a fault signal of an antifriction bearing. The method comprises that a vibration signal of the antifriction bearing is obtained, and continuous wavelet transform is carried out on the collected vibration signal to obtain a time-frequency graph; auto-correlation operation is carried out on a wavelet coefficient corresponding to each frequency in the time-frequency graph, noise interference is removed by filtering, and a periodic fault component is extracted; and Hilbert transform is used to carry out envelop demodulation, Fourier transform is carried out to obtain an envelope power spectrum, and a fault characteristic frequency is obtained. The method can be used to extract the weak periodic impact component from an early-stage fault of the antifriction bearing, and provides key support for state monitoring and fault diagnosis of machines.

Description

technical field [0001] The invention belongs to the field of mechanical equipment signal processing, and in particular relates to a feature extraction method and equipment for weak fault signals of rolling bearings. Background technique [0002] Rolling bearings are one of the most widely used and most critical parts in rotating machinery. Its operating status often determines the performance of the whole machine. Any slight fault will have a great impact on the stability of the machine. Rolling bearings are easily damaged during operation. 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 effective and reasonable solutions for the fault. Maintenance plan, thereby prolonging the life of the machine and greatly reducing the harm caused by failure. [0003] When the rolling bearing is partiall...

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Problems solved by technology

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, and the fault features extracted on different frequency band components are easily detected by frequency conversion and machine operation 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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