Abstract
When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time–time (IHTT) transform, by combining a Hilbert time–time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures.
Highlights
As a key component of rotating machinery, rolling bearings are responsible for ensuring the rotation accuracy of shafts and providing stable support for machines, so their running state is directly related to the performance of the entire system [1,2,3]
The proposed method was verified using two experimental samples: (a) An outer race (OR) fault signal downloaded from the bearing data center of Case Western Reserve University (CWRU) [43]; (b) An inner race (IR) fault signal obtained by experimental simulation
An improved Hilbert time–time (IHTT) transform method was proposed in this paper to enhance and extract the weak fault features of defective bearings by combing principal component analysis (PCA) with an Hilbert time–time (HTT) transform
Summary
As a key component of rotating machinery, rolling bearings are responsible for ensuring the rotation accuracy of shafts and providing stable support for machines, so their running state is directly related to the performance of the entire system [1,2,3]. One challenge of frequency domain analysis techniques is that fault feature signals are very weak relative to background noise and other interferences in the early damage stage; conventional spectrum methods will lose efficacy in their fault diagnosis of rolling bearings. Based on the TT transform, a Hilbert TT (HTT) transform was proposed by Xianfeng Fang and Ming J Zuo [37] to extract the modulating signal for gearbox fault detection by combining a HT with TT transform. This new method was introduced to reveal the impulses buried in the vibration signals of faulty bearings.
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