Name
Title | ||
|---|---|---|
Adaptive cancellation of background machine noise based on combination of ICA-R and RBFNN |
Abstract | ||
|---|---|---|
Extraction of machine fault signals from background machine noises is of great help in improving the accuracy of machine fault diagnosis. In this paper, a prediction model of time series based on RBF neural network (RBFNN) is proposed to learn the priori knowledge of background machine noise that obscure in a template noise which is tailored from the historical samples of background machine noises. By defining the mean square error of prediction to candidate independent component with the proposed RBFNN model as the contrast function, a new ICA-R algorithm is proposed to extract the `pure' background machine noise which is then taken as reference input of a Volterra Adaptive Noise Cancellation (VANC) system. The simulation shows that the combination of ICA-R and VANC system prevails over a standard VANC system. The new VANC system is easier to be implemented in engineering applications due to its sensor-position independent characteristics. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICNC.2012.6234616 | ICNC |
Keywords | Field | DocType |
rbf neural network,template noise,machine noise monitoring,radial basis function networks,vanc system,machine fault diagnosis accuracy improvement,background machine noises,mean square error,machine fault signal extraction,ica-r,adaptive noise cancellation,noise abatement,independent component analysis,radial basis function neural networks,failure analysis,fault diagnosis,reference input,ica with reference,engineering applications,independent component analysis-with-reference,volterra adaptive noise cancellation system,sensor-position independent characteristics,condition monitoring,rbfnn,historical samples,mechanical engineering computing,machine fault diagnosis,time series,mean square error methods,predictive models,engines,vectors,prediction model,noise measurement,time series analysis,noise | Computer science,Noise control,Divergence (statistics),Mean squared error,Artificial intelligence,Condition monitoring,Independent component analysis,Active noise control,Artificial neural network,Machine learning | Conference |
Volume | Issue | ISSN |
null | null | 2157-9555 |
ISBN | Citations | PageRank |
978-1-4577-2130-4 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Zhang | 1 | 272 | 42.02 |
| Zhenping Pang | 2 | 8 | 1.31 |
| Yaowu Shi | 3 | 4 | 4.20 |
| Luquan Ren | 4 | 2 | 4.76 |