Rolling element-bearing diagnostics has been studied over the last thirty years, and envelope analysis is widely recognized as being the best approach for detection and diagnosis of rolling element bearing incipient f...Rolling element-bearing diagnostics has been studied over the last thirty years, and envelope analysis is widely recognized as being the best approach for detection and diagnosis of rolling element bearing incipient failure. But one of the on-going difficulties with envelope technique is to determine the best frequency band to envelop. Here, wavelet transform technique is introduced into envelope analysis to solve the problem by capturing bearing defects-sensory scales (i.e. frequency bands). A modulated Gaussian function is chosen to be the analytical wavelet because it coincides well with bearing defect-induced vibration signal patterns. Vibration signals measured from railway bearing tests were studied by the proposed method. Cases of bearings with single and multiple defects on inner and outer race under different testing conditions are presented. Experimental results showed that the proposed method allowed a more accurate local description and separation of transient signal part, which were caused by impacts between defects and the mating surfaces in the bearing. The combination method provides an effective signal detection technique for rolling element-bearing diagnostics.展开更多
To early detect symptoms of defective rolling element bearings, this paper introduces discrete wavelet packet transform (DWPT)-based sub-band analysis. The objective of this analysis is to explore the impacts of mul...To early detect symptoms of defective rolling element bearings, this paper introduces discrete wavelet packet transform (DWPT)-based sub-band analysis. The objective of this analysis is to explore the impacts of multiple sub-band signals by 4-level DWPTusing proper Daubechies mother wavelet on a 2.5-second acoustic emission signal. In particular, the DWPT-based sub-bandanalysis determines the most informative sub-band signal involving intrinsic information about bearing defects among theaforementioned multiple sub-band signals based on the ratio of spectral magnitudes at harmonics of the bearing's characteristicfrequency to those around the harmonics. This paper also verifies the efficacy of the DWPT-based sub-band analysis for seededbearing defects (i.e., a crack on the inner race, the outer race, or a roller).展开更多
文摘Rolling element-bearing diagnostics has been studied over the last thirty years, and envelope analysis is widely recognized as being the best approach for detection and diagnosis of rolling element bearing incipient failure. But one of the on-going difficulties with envelope technique is to determine the best frequency band to envelop. Here, wavelet transform technique is introduced into envelope analysis to solve the problem by capturing bearing defects-sensory scales (i.e. frequency bands). A modulated Gaussian function is chosen to be the analytical wavelet because it coincides well with bearing defect-induced vibration signal patterns. Vibration signals measured from railway bearing tests were studied by the proposed method. Cases of bearings with single and multiple defects on inner and outer race under different testing conditions are presented. Experimental results showed that the proposed method allowed a more accurate local description and separation of transient signal part, which were caused by impacts between defects and the mating surfaces in the bearing. The combination method provides an effective signal detection technique for rolling element-bearing diagnostics.
文摘To early detect symptoms of defective rolling element bearings, this paper introduces discrete wavelet packet transform (DWPT)-based sub-band analysis. The objective of this analysis is to explore the impacts of multiple sub-band signals by 4-level DWPTusing proper Daubechies mother wavelet on a 2.5-second acoustic emission signal. In particular, the DWPT-based sub-bandanalysis determines the most informative sub-band signal involving intrinsic information about bearing defects among theaforementioned multiple sub-band signals based on the ratio of spectral magnitudes at harmonics of the bearing's characteristicfrequency to those around the harmonics. This paper also verifies the efficacy of the DWPT-based sub-band analysis for seededbearing defects (i.e., a crack on the inner race, the outer race, or a roller).