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CIRP Conference on Intelligent Computation in Manufacturing Engineering Wavelet Based Denoising in Manufacturing and in Business
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bényász, Gábor Cser, L. |
| Copyright Year | 2012 |
| Abstract | Last two decades a powerful new tool has appeared i n the quality assurance and quality control: the da ta mining. It enables discovering the hidden connections and confluences in big data systems, collected by on-line measuring a d control systems in manufacturing processes. The main difficulties appe r in distinguishing the valuable signal and the no ises in measurements, as well as the time dependent, relatively slow changes indi cating the inherent tendency of changes inside the process. It has been shown ([1], [2] etc.) that the Self Org anizing Maps can be used effectively in analyzing t he manufacturing process (e.g. hot rolling) in order of predicting the expected qu ality. Source of the analysis is the big amount of data collected by the hierarchically structured control system of the hot rolling mill. Since the sampling frequencies in di fferent measurement devices were different, the problem of the possible noise b ecame vital for the reliability of quality predicti on. Recent paper intends to illustrate the efficiency of the wavelet analysis for noise removal in differen t areas of data mining, including the thermal processes in hot rolling, and in another area, very far from it – in financial a nalysis. Technically, the wavelet is based on the usage of o rthogonal functions. Unlike the similar and mathema tically closest Fourier analysis wavelets conserve the entirely time depe ndent information, where the temporal identificatio n f the signals frequency content is also important. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of CIRP ICME |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://real.mtak.hu/34065/1/cser_benyasz_0419.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
