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.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Unsupervised Spectral Feature Selection with Dynamic Hyper-graph Learning ABSTRACT: In order to produce interpretable and discriminative results from unsupervised spectral feature selection (USFS) methods, an embedding
PROJECT TITLE : Joint detection and matching of feature points in multimodal images ABSTRACT: In this work, we propose a novel architecture for Convolutional Neural Networks (CNNs) for the joint detection and matching of feature
PROJECT TITLE : Prior Guided Feature Enrichment Network for Few-Shot Segmentation ABSTRACT: Methods of semantic segmentation that have advanced to the state-of-the-art require a sufficient amount of labeled data to achieve good
PROJECT TITLE : Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM ABSTRACT: In this paper, we present a novel unsupervised feature learning architecture that consists of a multi-clustering integration
PROJECT TITLE : Small Low-Contrast Target Detection Data-Driven Spatiotemporal Feature Fusion and Implementation ABSTRACT: An essential and difficult task in the airspace is the detection of low-contrast targets that are relatively

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry