A Bearing Composite Fault Diagnosis Method Based on Zero Space Differential Operator and Blind Source Separation

Abstract

The invention discloses a null space differential operator and blind source separation based bearing combined fault diagnosis method and belongs to the technical field of bearing fault diagnosis. By means of null space differential operators based on bearing fault features, bearing fault signals (random vibration, fault shock components and noises caused by rotation of a normal part of a bearing) are resolved into a series of local narrow-band signals (comprising components of fault features); subsequently, the obtained narrow-band signals and bearing signals are regarded as one group of observation signals; by means of a blind source separation algorithm, the observation signals are subjected to blind source separation to achieve separation of combined faults of the bearing; finally, the signals obtained from separation are subjected to Hilbert demodulation processing, and afterwards, fault features of the bearing are extracted to ultimately achieve combined fault diagnosis of the bearing. In new observation signals, the observation signals are more than source signals, such that a premise required by the blind source separation algorithm can be satisfied, and further, combined fault separation and feature extraction of the bearing are achieved.

Description

technical field [0001] The invention relates to a bearing compound fault diagnosis method, in particular to a bearing compound fault diagnosis method based on a zero-space differential operator and blind source separation, and belongs to the technical field of bearing fault diagnosis. Background technique [0002] Bearings are one of the most widely used components in rotating machinery, and it is of great significance to detect and diagnose faults in their operating conditions. The fault vibration signal of the bearing is a typical non-stationary signal and contains a lot of noise and other interference signals. Its analysis and processing is a complex nonlinear problem, which is quite difficult, especially when the rolling bearing has a composite fault, the difference between different faults The mutual influence and interference of various fault characteristics make the fault characteristics complicated and bring great difficulties to accurate fault diagnosis. The signals...

AI Technical Summary

Problems solved by technology

The NSP algorithm has good robustness and adaptability, and has been well applied in the field of image processing, but it is still relatively rare in the field of mechanical fault diagnosis.

[0005] In recent years, the blind source separation algorithm has been applied to the field of mechanical fault diagnosis and has made some progress, but the existing blind source separation algorithm has certain deficiencies, the most important manifestation of which is that the blind source separation algorithm must The signal can be effectively separated under the premise that the number is greater than the number of source signals, which is difficult to meet in engineering practice. When the number of collected bearing fault signals is less than the number of fault source signals, the composite fault of the bearing cannot be separated using the blind source separation algorithm

Images