Fault detection using online selected data and updated regression models
| Autor: | Ernst Stadlober, Doris Schadler |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Applied Mathematics 020208 electrical & electronic engineering 010401 analytical chemistry Statistical model Regression analysis 02 engineering and technology Special Interest Group Condensed Matter Physics Residual computer.software_genre 01 natural sciences Fault detection and isolation 0104 chemical sciences 0202 electrical engineering electronic engineering information engineering A priori and a posteriori Leverage (statistics) Data mining Electrical and Electronic Engineering Error detection and correction Instrumentation computer |
| Zdroj: | Measurement. 140:437-449 |
| ISSN: | 0263-2241 |
| Popis: | Since their increasing complexity and the impossibility of monitoring the data manually, fault detection is of special interest at engine test beds. The work with research data presents yet another challenge. First, it is hard to establish physical models, at least for all variables of interest. Second, prior knowledge of all data settings is not available. Hence, this paper introduces statistical models with no a priori knowledge which are updated online to adapt to new data settings. In order to reduce the amount of time and memory required, incoming data is filtered according to different criteria. Several data selection criteria based on well-known statistical measures such as leverage or Cook’s distance have been tested on four different data sets. Our new proposal, a proper combination of the leverage measure and the forecast residual, performs the best. The models are still applicable to forecasting yet even more suitable for error detection. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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