Abstract
A method of combining autocorrelation function with cyclostationary theory and Hilbert envelope analysis is proposed and applied to extract characteristic frequency of rolling bearing. Meanwhile, mean power ratio is calculated and used to identify the fault types of aero-engine rolling bearing based on single-channel casing vibration signal. To verify the effectiveness of proposed method, a comparing analysis is carried out between traditional studies and proposed new method. Furthermore, the influences on the extraction of characteristics and calculation of mean power ratio are taken into account, including the ones of sensor installation position, fault types, type of experiment rigs, failure mode and rotational speed of rolling bearing. The result shows that the proposed method can diagnose running conditions and identify fault types of rolling bearing accurately and effectively just by single-channel casing vibration signal.
Highlights
State monitoring of large-scale rotating machinery, like aero-engine, is usually based on casing signal due to inconvenience of disassembly and limitation of structure
A robust condition monitoring method for rolling element bearings which employ a novel empirical mode decomposition and discrete wavelet packet transform was proposed, and the results show that the proposed method outperforms the conventional schemes by achieving up to over 23 % higher mean-peak ratio values [1]
In the normal running conditions of rolling bearing and in different cyclic frequency positions, no matter by extraction characteristic frequency based on slice signal’s Hilbert envelope spectrum or calculation of mean power ratio of different feature frequencies, all has shown that rolling bearing is running in normal conditions by proposed method, which is corresponding with real running conditions of rolling bearing
Summary
State monitoring of large-scale rotating machinery, like aero-engine, is usually based on casing signal due to inconvenience of disassembly and limitation of structure. The cyclic frequency is a mapping of original signal frequency, which results in the presence of some additional frequency components in cyclic frequency domain, which are otherwise not present in original signal frequency All these factors make it more difficult to apply cyclostationary theory to rolling bearing fault diagnosis in real aero-engine. A way of combining autocorrelation function of signal, Hilbert envelope analysis and cyclostationary theory was proposed and verified in this paper for monitoring running states and identifying fault types of rolling bearing of aero-engine according to extracted feature frequency or calculation of mean power ratio. The influences on the extraction of characteristics and calculation of mean power ratio are considered including the ones of sensor installation position, failure type and rotational speed
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