Abstract
Faults in rolling element bearings often cause the breakdown of rotating machinery. Not only the fault type identification but also the fault severity assessment is important. So this paper emphasizes the fault severity assessment. The method proposed in this paper contains two steps: first, identify the fault type based on the combination of empirical mode decomposition (EMD) and fast kurtogram; Second, assess the fault severity. In the first step, the original signal is firstly decomposed into some intrinsic mode functions (IMFs) and the representative IMFs are selected based on correlation analysis, and then the reconstruction signal (RS) is generated; Secondly, the fast kurtogram method is applied to the RS, and the optimum band width and center frequency is obtained. The fault type can be identified based on the fault characteristic frequency marked in the envelope demodulation spectrum. In the second step, the energy percentage of the most fault-related IMF is chosen as an indicator of the fault severity assessment. Experimental data of rolling element bearings inner raceway fault (IRF) with three severities at four running speeds were analyzed. The results show that the IRF identification and fault severity assessment is realized. The breakthrough attempt provides the great potential in the application of condition monitoring of bearings.
Highlights
As an important part of rotating machinery, rolling element bearing is one of the most common fault sources of equipment
To learn about dyadic wavelet decomposition algorithm, firstly, the original signal is decomposed into a series of frequency-band signals, the kurtosis value on each frequency band is calculated in the following; Secondly, a kurtosis vs frequency diagram is generated, from which where there is the biggest kurtosis value, and there is the optimal center frequency and bandwidth ;Thirdly, the signal is filtered based on the optimal center frequency and bandwidth,and demodulated by square enveloping; the characteristic frequency is gained by spectrum analysis and the faults can be diagnosed
There are different fault characteristic frequencies associated with different parts of the bearings, for example, Ball Passing Frequency Outer Race (BPFO), Ball Passing Frequency Inner Race (BPFI), and Ball Fault Frequency (BFF), which are associated with the outer race, the inner race and the ball respectively
Summary
As an important part of rotating machinery, rolling element bearing is one of the most common fault sources of equipment. FAULT IDENTIFICATION AND SEVERITY ASSESSMENT OF ROLLING ELEMENT BEARINGS BASED ON EMD AND FAST KURTOGRAM. Antoni [23] proposed a fast algorithm for computing the kurtogram and provided a good application in rolling element bearings faults diagnosis. This was achieved by providing the optimal center frequency and band width. According to the discussions above, in this paper, a new method combining EMD and Fast Kurtogram is applied to rolling element bearings inner raceway fault diagnosis.
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