Abstract: Maintenance and design engineers have great concern
for the functioning of rotating machineries due to the vibration
phenomenon. Improper functioning in rotating machinery originates
from the damage to rolling element bearings. The status of rolling
element bearings require advanced technologies to monitor their
health status efficiently and effectively. Avoiding vibration during
machine running conditions is a complicated process. Vibration
simulation should be carried out using suitable sensors/ transducers to
recognize the level of damage on bearing during machine operating
conditions. Various issues arising in rotating systems are interlinked
with bearing faults. This paper presents an approach for fault
diagnosis of bearings using neural networks and time/frequencydomain
vibration analysis.
Abstract: Occurrence of a multiple-points fault in machine operations could result in exhibiting complex fault signatures, which could result in lowering fault diagnosis accuracy. In this study, a multiple-points defect model (MPDM) is proposed which can simulate fault signature-s dynamics for n-points bearing faults. Furthermore, this study identifies that in case of multiple-points fault in the rotary machine, the location of the dominant component of defect frequency shifts depending upon the relative location of the fault points which could mislead the fault diagnostic model to inaccurate detections. Analytical and experimental results are presented to characterize and validate the variation in the dominant component of defect frequency. Based on envelop detection analysis, a modification is recommended in the existing fault diagnostic models to consider the multiples of defect frequency rather than only considering the frequency spectrum at the defect frequency in order to incorporate the impact of multiple points fault.
Abstract: Wavelet transform has been extensively used in
machine fault diagnosis and prognosis owing to its strength to deal
with non-stationary signals. The existing Wavelet transform based
schemes for fault diagnosis employ wavelet decomposition of the
entire vibration frequency which not only involve huge
computational overhead in extracting the features but also increases
the dimensionality of the feature vector. This increase in the
dimensionality has the tendency to 'over-fit' the training data and
could mislead the fault diagnostic model. In this paper a novel
technique, envelope wavelet packet transform (EWPT) is proposed in
which features are extracted based on wavelet packet transform of the
filtered envelope signal rather than the overall vibration signal. It not
only reduces the computational overhead in terms of reduced number
of wavelet decomposition levels and features but also improves the
fault detection accuracy. Analytical expressions are provided for the
optimal frequency resolution and decomposition level selection in
EWPT. Experimental results with both actual and simulated machine
fault data demonstrate significant gain in fault detection ability by
EWPT at reduced complexity compared to existing techniques.