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 :Text Mining Based on Tax Comments as Big Data Analysis Using SVM and Feature Selection - 2018ABSTRACT:The tax provides an important role for the contributions of the economy and development of a rustic. The improvements
PROJECT TITLE :Distributed Feature Selection for Efficient Economic Big Data Analysis - 2018ABSTRACT:With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although
PROJECT TITLE :Automatic Feature Selection Technique for Next Generation Self-Organizing Networks - 2018ABSTRACT:Despite self-organizing networks (SONs) pursue the automation of management tasks in current cellular networks, the
PROJECT TITLE :Large-Scale Kernel-Based Feature Extraction via Low-Rank Subspace Tracking on a Budget - 2018ABSTRACT:Kernel-primarily based ways get pleasure from powerful generalization capabilities in learning a selection of
PROJECT TITLE :Feature Map Quality Score Estimation Through Regression - 2018ABSTRACT:Understanding the visual quality of a feature map plays a important role in many active vision applications. Previous works mostly rely on object-level

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

Project Enquiry