Abstract
The autocorrelation function is combined with wavelet transform and cyclostationary theory (WT-AF-CT) in place of threshold denoising, and meanwhile the mean power ratio (MPR) is calculated by the proposed method. Furthermore, extracted characteristic as well as calculated MPR is used to identify compound faults of rolling bearings in aero-engine based on casing vibration acceleration signal-including the ones of common rolling bearing (inner race rotates and outer race is constant) and intershaft bearing (co-rotates with outer and inner race). A comparative analysis was carried out between conventional researches (cyclostationary theory (CT) or wavelet transform combined with threshold value denoising (WT-TD)) and proposed WT-AF-CT method. Additionally, the effect of sensors installation direction for feature separation and extraction of compound faults is considered. The results indicate that the proposed WT-AF-CT method can separate and extract characteristics of compound faults exactly and identify fault types of bearings precisely, no matter sensors are installed horizontally or vertically, while CT or WT cannot.
Highlights
The fault modes of rolling bearing are usually compound in real aero-engines due to the intercoupling among faults [1, 2]
(2) In Fig. 1(b), specific steps of characteristic extraction with the proposed Wavelet transforms (WT)-Autocorrelation function (AF)-CT method are as follow: Firstly, subject vibration signals of casing to time-frequency local decomposition based on wavelet transform; secondly, characteristic enhancement and denoising of detail signals and approximate signals after wavelet transform with autocorrelation function; thirdly, detail signals or approximate signals of better characteristic extraction effect are given slice analysis based on cyclic autocorrelation function; fourthly, calculate the frequency spectrum of slice signals
The feature frequency of inner race, outer race and rolling element of intershaft bearing is respectively equal to 2562.2 Hz, 2162.8 Hz and 924.8 Hz by calculation according to table.2 and Eqs. (10-12)
Summary
The fault modes of rolling bearing are usually compound in real aero-engines due to the intercoupling among faults [1, 2]. A STUDY ON THE FEATURE SEPARATION AND EXTRACTION OF COMPOUND FAULTS OF BEARINGS BASED ON CASING VIBRATION SIGNALS. Liao Chuanjun, et al analyzed the signal characteristics of acoustic emission (AE) and firstly proposed the analysis method of wavelet redistribution scale spectrum based on AE signals according to the extraction principles of fault characteristics of AE signals and features as a result of mechanical faults or injury, and applied the method to the identification of injury type and parts of rolling bearings in acoustic emission inspection [22]. Considering approximately symmetric physical structure and rotation way of aero-engine, the analysis of vibration signals of casing within the framework of cyclostationary theory can produce more precise status monitoring and fault identification of bearings. The effect of installation direction of sensors on feature separation and extraction is considered
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