PROJECT TITLE :
Feature Identification With Compressive Measurements for Machine Fault Diagnosis
Machine fault diagnosis collects huge amounts of vibration knowledge about complicated mechanical systems. Performing feature detection from these data sets has already led to a major challenge. Compressive sensing theory may be a new sampling framework that has an alternative to the well-known Shannon sampling theory. This theory permits the recovery of sparse or compressible signals from a small set of nonadaptive linear measurements. However, it's suboptimal to recover the whole signals from the compressive measurements and then solve feature identification problems through ancient DSP techniques. Thus, a completely unique mechanical feature identification technique is proposed during this paper. Its main advantage is that fault features are extracted directly within the compressive measurement domain while not sacrificing accuracy, while a important reduction in the dimensionality of the measurement information is achieved. Moreover, Gaussian white noises are significantly alleviated, which dramatically enhances the reliability of machine fault diagnosis. Parameter analysis is also profoundly investigated through a set of numerical experiments. Numerical simulations and experiments are any performed to prove the reliability and effectiveness of the proposed method.
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