Monitoring nonstationary and dynamic trends for practical process fault diagnosis
| Autor: | Andrew Ball, Qian Chen, Fengshou Gu, Uwe Kruger, Yuanling Lin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Cointegration Basis (linear algebra) Computer science Process (engineering) Applied Mathematics 020208 electrical & electronic engineering Multivariate normal distribution 02 engineering and technology Fault (power engineering) computer.software_genre Computer Science Applications Variable (computer science) 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Data mining Electrical and Electronic Engineering computer |
| Zdroj: | Control Engineering Practice. 84:139-158 |
| ISSN: | 0967-0661 |
| DOI: | 10.1016/j.conengprac.2018.11.020 |
| Popis: | This article introduces a revised common trend framework to monitor nonstationary and dynamic trends in industrial processes and shows needs for each improvement on the basis of three application studies. These improvements relate to (i) the extension of the common trend framework to include sets that contain stationary and nonstationary variables, (ii) handling cases where residuals are not drawn from multivariate normal distributions and (iii) the application of the framework to larger variable sets. Existing work does not adequately address these practically important issues. Industrial application studies highlight the needs for (i) the extended framework to model data sets containing stationary and nonstationary variables, (ii) handling statistics that are not based on normally distributed residuals and (iii) the use of Chigira procedure to robustly extract common trends. The extended framework is compared to traditional approaches. |
| Databáze: | OpenAIRE |
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