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 Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering ABSTRACT: In recent years, detecting anomalous electricity usage behavior has become increasingly important. Existing
PROJECT TITLE : Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification ABSTRACT: The problem of unbalanced data is common in network intrusion detection, spam filtering,
PROJECT TITLE : Financial Latent Dirichlet Allocation (FinLDA) Feature Extraction in Text and Data Mining for Financial Time Series Prediction ABSTRACT: Many financial time series predictions based on fundamental analysis have
PROJECT TITLE : OFS-NN An Effective Phishing Websites Detection Model Based on Optimal Feature Selection and Neural Network ABSTRACT: Phishing attacks have become a major menace to people's daily lives and social networks. Attackers
PROJECT TITLE : Robust Unsupervised Multi-view Feature Learning with Dynamic Graph ABSTRACT: By modeling the affinity associations with a graph to lower the dimension, graph-based multi-view feature learning algorithms learn a

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

Project Enquiry