Bearing quantitative diagnosis method based on morphological filtering and complexity measure

Abstract

The invention relates to a bearing quantitative diagnosis method based on morphological filtering and complexity measure, comprising the following steps: firstly selecting Morlet wavelet and Laplace wavelet as the structural elements of a morphological filter; utilizing morphological filtering method based on immue optimization to carry out time domain filtering processing on vibration signal of a rolling bearing obtained through collection; and then adopting an algorithm based on improved complexity measure to carry out quantitative evaluation on the vibration signal of the rolling bearing after filtering. According to the method, the fault grade of the rolling bearing is evaluated from a qualitative angle, and the bearing data processed through complexity measure has the characteristic of monotonicity and can be used for indicating the real-time running state of the bearing to monitor; and therefore, the accuracy of the fault diagnosis of the rolling bearing is improved, and the on-site maintenance is facilitated.

Description

technical field [0001] The invention belongs to the field of mechanical fault diagnosis, in particular to a bearing quantitative diagnosis method based on morphological filtering and complexity measurement. Background technique [0002] Rolling bearings are parts widely used in rotating machinery, and their operating status directly affects the performance of the equipment. At present, most of the monitoring technologies for rolling bearing operating status at home and abroad are qualitative analysis methods. These methods first obtain the signal characteristics reflecting the operating status of the bearing, and compare the analysis results with typical faults (the essence of fault diagnosis is pattern recognition. The essence is comparative judgment), which can judge whether there is a fault in the bearing and the type of fault. However, this kind of qualitative diagnosis method is not enough for the preventive maintenance of the rolling bearing. It is necessary to find a ...

AI Technical Summary

Problems solved by technology

At present, most of the monitoring technologies for rolling bearing operating status at home and abroad are qualitative analysis methods. These methods first obtain the signal characteristics reflecting the operating status of the bearing, and compare the analysis results with typical faults (the essence of fault diagnosis is pattern recognition. The essence is comparative judgment), which can judge whether there is a fault in the bearing and the type of fault. However, this kind of qualitative diagnosis method is not enough for the preventive maintenance of the rolling bearing. It is necessary to find a quantitative index that reflects the operating state of the rolling bearing, that is, to grasp the fault degree of the rolling bearing, in order to be more effective. Realize rolling bearing condition monitoring and fault diagnosis

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