Abstract
In an engineering practice, the faults of rolling bearing are mostly represented as being compound and hard to diagnose. For that, intrinsic time-scale decomposition (ITD) algorithm was combined with Auto-correlation Function (AF) to extract the characteristics of compound faults of rolling bearing in aviation engine. Firstly, ITD algorithm was used to decompose acceleration signal into multiple rotational and residual trend component; secondly, rotational components were reconstructed to figure out their AF; finally, characteristic frequency of rolling bearing under compound faults mode was extracted by Hilbert spectrum envelope. To validate the effectiveness of the method, a comparative study on sensor installation positions and vibration acceleration signal of different compound faults has been carried out. The result of study shows that the proposed ITD-AF method is capable to extract compound fault characteristics of rolling bearing in an effective and precise manner and the installation positions of sensors, rotation speed and fault type shows insensitivity to extraction.
Highlights
Rolling bearing is one of critical components of aero motor, for which it becomes very important to have effective monitoring on its running status
Liu Xiaofeng, et al brought forward a fault diagnosis method basic on wavelet packet and empirical mode decomposition [1], the diagnosis shows that the fault frequency can be extracted effectively, and it is easy to judge and distinguish the fault type; envelope analysis of wavelet packet was applied to fault diagnosis of rolling bearing by researchers [2, 3], for example, an automatic diagnosis method basic on wavelet packet coefficients, kurtosis and envelope analysis was proposed according to the vibration characteristics of rolling bearing; Li Yongbo, et al put forward an improved Empirical Mode Decomposition methodology to validate its effectiveness [4], compared with the EMD algorithm, it shows that the improved algorithm in the field of fault diagnosis is applicable
intrinsic time-scale decomposition (ITD) algorithm is combined with Auto-correlation Function (AF) to complete the research on extracting the characteristic frequency of rolling bearings under the different compound fault types
Summary
Rolling bearing is one of critical components of aero motor, for which it becomes very important to have effective monitoring on its running status. In the fault diagnosis of bearings, Cheng Junsheng, et al decomposed vibration signal of rolling bearings with ITD algorithm and got several components of proper rotation, the ones of which containing fault information were extracted for permutation entropy as fault characteristic value; classifier of Variable Predictive Model Based Class Discriminate was trained and at last, which was introduced to make fault diagnosis and classification [15], the experimental results show that this method can be effectively used for fault diagnosis of rolling bearing. The effects of noise, interaction between components and signal transmission contributes to the fact that the faults of rolling bearings tend to be complex and weak in the vibration signal. ITD algorithm is combined with AF to complete the research on extracting the characteristic frequency of rolling bearings under the different compound fault types
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
