Reliability of analytical systems: use of control charts, time series models and recurrent neural networks (RNN)
| Autor: | M.P. Callao, Itziar Ruisánchez, F.X. Rius, A. Rius |
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| Rok vydání: | 1998 |
| Předmět: |
Series (mathematics)
Computer science Process (engineering) business.industry Process Chemistry and Technology Control engineering Statistical process control Machine learning computer.software_genre Field (computer science) Computer Science Applications Analytical Chemistry Recurrent neural network Control chart Artificial intelligence business computer Spectroscopy Software Reliability (statistics) Advanced process control |
| Zdroj: | Chemometrics and Intelligent Laboratory Systems. 40:1-18 |
| ISSN: | 0169-7439 |
| DOI: | 10.1016/s0169-7439(97)00085-3 |
| Popis: | In this tutorial, the techniques used to study the reliability of analytical systems over time are discussed. The most classical approach is to use statistical process control (SPC) with control charts, and its principal characteristics, benefits and limitations are shown. The advanced process control (APC) approach, developed and mainly used in the field of engineering, is also studied and its possibilities for monitoring chemical measurement processes evaluated. The fundamentals and potentialities of recurrent neural networks (RNN) in this field are also presented. The bases of these three approaches are described, and their advantages and drawbacks discussed. They are applied to a simulated time series and to real process analytical data, and the results obtained for these data are compared. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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