Rolling bearing fault diagnosing method and equipment based on CEEMDAN and CFSFDP

Abstract

The invention discloses a rolling bearing fault diagnosing method and equipment based on CEEMDAN and CFSFDP, belonging to the field of fault diagnosis of rotary machines. The method comprises the steps of acquiring vibration signals of a bearing in a normal state and different fault mode states so as to obtain sample points of the vibration signals of different states, decomposing by using CEEMDANto obtain time and frequency domains characteristics of bearing diagnosis and screening out bearing state representation parameters along with the time domain and frequency domain characteristics, dividing the representation parameters into training samples and a test samples, then using a table CFSFDP algorithm as a bearing fault diagnosis model, inputting the training samples into the bearing fault diagnosis model, clustering output results, obtaining clustering amount, clustering center points of each type, and state types corresponding to the clustering center points, and inspecting the trained diagnosis model by using test samples. The method and equipment can identify different bearing fault types and fault degrees accurately and effectively.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of rotating machinery, more specifically, relates to a complete empirical mode decomposition (Complete Ensemble Empirical Mode Decomposition with AdaptiveNoise, CEEMDAN) algorithm based on adaptive noise and clustering (Clustering By fast search and find of density peaks, a new method for bearing fault diagnosis based on CFSFDP) algorithm. Background technique [0002] Bearings are one of the most common components of rotating machinery, and their working conditions directly affect the reliability and safety of the entire rotating machinery. Once a bearing fails, it is necessary to diagnose the location and cause of the failure in a timely and accurate manner. This is of great practical significance for improving the maintenance efficiency of rotating machinery, reducing its maintenance cost, and ensuring its long-term stable operation. [0003] Bearing fault diagnosis methods based on vibration s...

AI Technical Summary

Problems solved by technology

However, in practical situations, due to the diversity of faults, it is unrealistic to know the number of fault modes in advance

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