Envelope analysis is a widely used method for rolling element bearing fault detection. To obtain high detection accuracy, it is critical to determine an optimal frequency narrowband for the envelope demodulation. However, many of the schemes which are used for the narrowband selection, such as the Kurtogram, can produce poor detection results because they are sensitive to random noise and aperiodic impulses which normally occur in practical applications. To achieve the purposes of denoising and frequency band optimisation, this paper proposes a novel modulation signal bispectrum (MSB) based robust detector for bearing fault detection. Because of its inherent noise suppression capability, the MSB allows effective suppression of both stationary random noise and discrete aperiodic noise. The high magnitude features that result from the use of the MSB also enhance the modulation effects of a bearing fault and can be used to provide optimal frequency bands for fault detection. The Kurtogram is generally accepted as a powerful means of selecting the most appropriate frequency band for envelope analysis, and as such it has been used as the benchmark comparator for performance evaluation in this paper. Both simulated and experimental data analysis results show that the proposed method produces more accurate and robust detection 2 results than Kurtogram based approaches for common bearing faults under a range of representative scenarios.
Abstract-This paper presents a novel method for diagnosing combination faults in planetary gearboxes. Vibration signals measured on the gearbox housing exhibit complicated characteristics because of multiple modulations of concurrent excitation sources, signal paths and noise. To separate these modulations accurately, a modulation signal bispectrum based sideband estimator (MSB-SE) developed recently is used to achieve a sparse representation for the complicated signal contents, which allows effective enhancement of various sidebands for accurate diagnostic information. Applying the proposed method to diagnose an industrial planetary gearbox which coexists both bearing faults and gear faults shows that the different severities of the faults can be separated reliably under different load conditions, confirming the superior performance of this MSB-SE based diagnosis scheme.
This paper presents investigations into the influences of bearing clearances on the diagnostic features of monitoring rolling-bearings. A nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed for numerical analysis under increased radial clearances which are due to not only the scenarios of bearing grades but also gradual wear with bearing service lifetime. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics under different fault cases along with clearance changes. Numerical results show that the vibrations at fault characteristic frequencies exhibit clear inconsistency with common understandings for different cases of increased clearances. This study highlights that it has to take into account the clearance effect, especially for the inner race fault, in order to avoid the under-estimate of fault sizes which may be indicated by the feature amplitude reduction.Keywords: roller element bearing; nonlinear dynamic model; radial clearance; Hertzian contact deformation; condition monitoring. The influence of rolling bearing clearances on diagnostic signatures 17Reference to this paper should be made as follows: Rehab, I., Tian, X., Gu, F. and Ball, A.D. (2018) MSc from the Taiyuan University of Technology. He has published over 200 technical publications in machinery diagnosis, signal processing, measurement systems and dynamic modelling. He has undertaken vibro-acoustic research projects of turbine machines, electric motors, internal combustion engines, reciprocating compressors, centrifugal pumps, hydraulic power systems, gearboxes and bearings. Recent research interests are mainly on power supply data and tribological behaviour analysis-based system diagnostics and monitoring.Andrew D. Ball is a Professor of Diagnostics Engineering from the University of Huddersfield, PhD from the University Manchester and BS from the University of Leeds. He is one of UK's foremost experts in the fields of machinery diagnostics, dynamic modelling, intelligent computation and vibro-acoustics analysis, with over 30 years of maintenance engineering experience. He is the author of over 300 technical publications in machine diagnosis, non-destructive measurement and related fields, and he spends much of his time lecturing and consulting to industry in all parts of the world. He has been the organiser of several international conferences in condition monitoring and maintenance.
For accurate fault detection and diagnosis, this paper focuses on the study of bearing vibration responses under increasing radial clearances due to investable wear and different bearing grades. A nonlinear dynamic model incorporating with local defects and clearance increments is developed for a deep groove ball bearing. The model treats the inner race-shaft and outer race-housing as two lumped masses which are coupled by a nonlinear spring formalized by the Hertzian contact deformation between the balls and races. The solution of the nonlinear equation is obtained by a Runge-Kutta method in Matlab. The results show that the vibrations at fault characteristic frequencies exhibit significant changes with increasing clearances. However, an increased vibration is found for the outer race fault whereas a decreased vibration is found for inner race fault. Therefore, it is necessary to take into account these changes in determining the size of faults.
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