Ranking of Toxic Compound Concentrations As Diagnostic Parameetrs of Marine Internal Combustion Engine
| Autor: | Tomasz Kniaziewicz, R. Zadrąg |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
020209 energy Naval architecture. Shipbuilding. Marine engineering VM1-989 Value (computer science) Ocean Engineering 02 engineering and technology computer.software_genre Diesel engine Measure (mathematics) Set (abstract data type) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Sensitivity (control systems) Structure (mathematical logic) 050210 logistics & transportation exhaust components ranking of parameters Mechanical Engineering 05 social sciences Ranking Internal combustion engine Data mining diagnostic model computer diesel engine |
| Zdroj: | Polish Maritime Research, Vol 25, Iss s1, Pp 234-242 (2018) |
| ISSN: | 2083-7429 |
| DOI: | 10.2478/pomr-2018-0047 |
| Popis: | Changing selected engine structure parameters, especially fuel system parameters, affects the emission of harmful compounds in the exhaust gas. Changes in harmful compound emission are frequently ambiguous, as they highly depend on parameters controlling the combustion process. An additional problem is that simple interactions are frequently accompanied with mutual influence of these parameters. Therefore, we can say about different sensitivity of diagnostic parameters to the same excitations coming from the engine structure but executed at different loading states. When the set of diagnostic parameters is numerous and the values of these parameters are similar, there is a real problem with their correct classification, frequently based on subjective assessment by the analyst. In the article, the authors propose a methodology to classify the recorded diagnostic parameters. In earlier works by the authors [4,6,7], the information capacity index method (the Hellwig method) was proposed as the measure of diagnostic parameter sensitivity. Based on this method, a rankling of diagnostic parameters can be created which divides the set of diagnostic variables into stimulators and destimulators. Novel authors’ approach to the presented problem consists in including nominants, i.e. variables with the most favourable value for the analysed aspect of the research, in the set of diagnostic variables. This normalisation of the set is believed to be helpful for making a diagnostic decision free from analyst’s arbitrariness. The zero unitarization method can also be helpful in creating diagnostic tests. |
| Databáze: | OpenAIRE |
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