Diagnosis for systems with multi-component wear interactions
| Autor: | Phil Scarf, Samia Nefti-Meziani, Roy Assaf, Phuc Do |
|---|---|
| Přispěvatelé: | University of Salford, Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Salford Business School, IEEE Reliability society, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Do, Phuc |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering 021103 operations research business.industry diagnosis 0211 other engineering and technologies Short-time Fourier transform Complex system Condition monitoring 02 engineering and technology Structural engineering multi-component system Reliability engineering Accelerated life testing [SPI.AUTO]Engineering Sciences [physics]/Automatic [SPI.AUTO] Engineering Sciences [physics]/Automatic 020901 industrial engineering & automation degradation modelling Component (UML) Frequency domain Prognostics rate-state interaction business Reliability (statistics) |
| Zdroj: | 2017 IEEE International Conference on Prognostics and Health Management, PHM2017 2017 IEEE International Conference on Prognostics and Health Management, PHM2017, IEEE Reliability society, Jun 2017, Dallas, Texas, United States ICPHM |
| Popis: | International audience; Predicting remaining useful lifetime is key to improving operational efficiency, and increasing the reliability of machinery. This paper presents an approach for increasing the accuracy of diagnostics of systems with multiple components. We first discuss a degradation model for systems, where the deterioration process of a component is influenced by the state of deterioration of the other components. Then, we present a gearbox accelerated life testing platform, where we collect vibration data from accelerometers mounted over each gear supporting shaft. Next, we provide our methodology of extracting health indicators, from systems with such complex wear interactions and noisy signals, using data pre-processing for denoising, and Short Time Fourier Transform (STFT). Finally, by using the approach introduced in this paper and the experimental results, we demonstrate the need for monitoring and modelling wear interdependencies in complex systems, over the conventional condition monitoring of components separately. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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