Vibration analysis for Bearing outer race condition diagnostics

Abstract

This paper investigates defect detection methodologies for rolling element bearings through vibration analysis. Specifically, the utility of a new signal processing scheme combining the High Frequency Resonance Technique (HFRT) and Adaptive Line Enhancer (ALE) is investigated. The accelerometer is used to acquire data for this analysis, and experimental results have been obtained for outer race defects. Results show the potential effectiveness of the signal processing technique to determine both the severity and location of a defect. The HFRT utilizes the fact that much of the energy resulting from a defect impact manifests itself in the higher resonant frequencies of a system. Demodulation of these frequency bands through use of the envelope technique is then employed to gain further insight into the nature of the defect while further increasing the signal to noise ratio. If periodic, the defect frequency is then present in the spectra of the enveloped signal. The ALE is used to enhance the envelope spectrum by reducing the broadband noise. It provides an enhanced envelope spectrum with clear peaks at the harmonics of a characteristic defect frequency. It is implemented by using a delayed version of the signal and the signal itself to decorrelate the wideband noise. This noise is then rejected by the adaptive filter that is based upon the periodic information in the signal. Results have been obtained for outer race defects. They show the effectiveness of the methodology to determine both the severity and location of a defect. In two instances, a linear relationship between signal characteristics and defect size is indicated

Condition Monitoring of Low Speed Bearings: A Comparative Study of the Ultrasound Technique Versus Vibration Measurements

“…The simplest method is to use overall root-mean-square (RMS) level and crest factor, i.e., the ratio of peak value to RMS. This method has been applied with limited success in the detection of localized defects [28][29][30]. Probability density has also been used popularly for bearing defect detection [8,9,29,31,32].…”

Evaluation of the use of envelope analysis and DWT on AE signals generated from degrading shafts

“…It would be costly and unrealistic in practice to determine the resonant modes through experiments on rotating machinery that may alter under different operational conditions. In addition, it is also difficult to estimate how these resonant modes are affected in the assembly of a complete bearing and mounting in a specific housing, even if the resonant frequencies of individual bearing elements can be tested or calculated theoretically [13]. As such, most researchers choose the band-pass range as an option.…”

Detection of faults in gearboxes using acoustic emission signal

“…It was concluded that AE amplitude and energy increased with increased pitting. In a separated study, Singh et al [11] studied the feasibility of AE for gear fault diagnosis. In one test, a simulated pit was introduced on the pitch line of a gear tooth using an electrical discharge machining (EDM) process.…”

“…These AE activities increased as the pitting spread over more teeth. Singh et al [11] concluded that AE could provide earlier detection over vibration monitoring for pitting of gears, but noted it could not be applicable to extremely high speeds or for unloaded gear conditions. Tan et al [12] offered that AE rms levels from the pinion were linearly correlated to pitting rates; AE showed better sensitivity than vibration at a higher torque level (220 Nm) due to fatigue gear testing using spur gears.…”