Advanced Process Monitoring for Industry 4.0
| Weitere Verfasser: |
Reis, Marco S.
, [HerausgeberIn]
Gao, Furong , [HerausgeberIn] |
|---|---|
| Umfang/Format: |
1 online resource (288 pages). |
| Schlagworte: | |
| Online Zugang: |
DOAB: download the publication DOAB: description of the publication |
| LEADER | 03852namaa2201201ui 4500 | ||
|---|---|---|---|
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| 005 | 20221228154426.0 | ||
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| 007 | cr|mn|---annan | ||
| 008 | 20220111s2021 xx |||||o ||| 0|eng d | ||
| 020 | |a books978-3-0365-2074-2 | ||
| 020 | |a 9783036520735 | ||
| 020 | |a 9783036520742 | ||
| 040 | |a oapen |c oapen |b eng |d DE-2553 |e rda | ||
| 024 | 7 | |a 10.3390/books978-3-0365-2074-2 |c doi | |
| 041 | 0 | |a eng | |
| 042 | |a dc | ||
| 072 | 7 | |a TB |2 bicssc | |
| 100 | 1 | |a Reis, Marco S. |e editor | |
| 264 | |b MDPI - Multidisciplinary Digital Publishing Institute, |c 2021. | ||
| 700 | 1 | |a Gao, Furong |e editor | |
| 700 | 1 | |a Reis, Marco S. |e other | |
| 700 | 1 | |a Gao, Furong |e other | |
| 245 | 1 | 0 | |a Advanced Process Monitoring for Industry 4.0 |
| 300 | |a 1 online resource (288 pages). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 653 | |a spatial-temporal data | ||
| 653 | |a pasting process | ||
| 653 | |a process image | ||
| 653 | |a convolutional neural network | ||
| 653 | |a Industry 4.0 | ||
| 653 | |a auto machine learning | ||
| 653 | |a failure mode effects analysis | ||
| 653 | |a risk priority number | ||
| 653 | |a rolling bearing | ||
| 653 | |a condition monitoring | ||
| 653 | |a classification | ||
| 653 | |a OPTICS | ||
| 653 | |a statistical process control | ||
| 653 | |a control chart pattern | ||
| 653 | |a disruptions | ||
| 653 | |a disruption management | ||
| 653 | |a fault diagnosis | ||
| 653 | |a construction industry | ||
| 653 | |a plaster production | ||
| 653 | |a neural networks | ||
| 653 | |a decision support systems | ||
| 653 | |a expert systems | ||
| 653 | |a failure mode and effects analysis (FMEA) | ||
| 653 | |a discriminant analysis | ||
| 653 | |a non-intrusive load monitoring | ||
| 653 | |a load identification | ||
| 653 | |a membrane | ||
| 653 | |a data reconciliation | ||
| 653 | |a real-time | ||
| 653 | |a online | ||
| 653 | |a monitoring | ||
| 653 | |a Six Sigma | ||
| 653 | |a multivariate data analysis | ||
| 653 | |a latent variables models | ||
| 653 | |a PCA | ||
| 653 | |a PLS | ||
| 653 | |a high-dimensional data | ||
| 653 | |a statistical process monitoring | ||
| 653 | |a artificial generation of variability | ||
| 653 | |a data augmentation | ||
| 653 | |a quality prediction | ||
| 653 | |a continuous casting | ||
| 653 | |a multiscale | ||
| 653 | |a time series classification | ||
| 653 | |a imbalanced data | ||
| 653 | |a combustion | ||
| 653 | |a optical sensors | ||
| 653 | |a spectroscopy measurements | ||
| 653 | |a signal detection | ||
| 653 | |a digital processing | ||
| 653 | |a principal component analysis | ||
| 653 | |a curve resolution | ||
| 653 | |a data mining | ||
| 653 | |a semiconductor manufacturing | ||
| 653 | |a quality control | ||
| 653 | |a yield improvement | ||
| 653 | |a fault detection | ||
| 653 | |a process control | ||
| 653 | |a multi-phase residual recursive model | ||
| 653 | |a multi-mode model | ||
| 653 | |a process monitoring | ||
| 653 | |a n/a | ||
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| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76899 |7 0 |z DOAB: description of the publication |
| 590 | |a Online publication | ||
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| 952 | |0 0 |1 0 |2 z |4 0 |6 ONLINE |7 1 |9 973761 |R 2022-12-28 14:44:26 |a DAIG |b DAIG |l 0 |o Online |r 2022-12-28 |y EB | ||
