Reliability Analysis Based on a Gamma-Gaussian Deconvolution Degradation Modeling with Measurement Error
| Autor: | Delia J. Valles-Rosales, Luis Alberto Rodríguez-Picón, Manuel Iván Rodríguez-Borbón, Luis Carlos Méndez-González, Iván Jc Pérez-Olguín, Roberto Romero-López |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Computer science QH301-705.5 Gaussian QC1-999 Gamma process gamma process 0211 other engineering and technologies 02 engineering and technology deconvolution reliability estimation 01 natural sciences 010104 statistics & probability symbols.namesake measurement system analysis General Materials Science 0101 mathematics Biology (General) Instrumentation QD1-999 Reliability (statistics) Fluid Flow and Transfer Processes lifetime Measurement systems analysis 021103 operations research Observational error Process Chemistry and Technology Physics General Engineering Engineering (General). Civil engineering (General) Computer Science Applications Chemistry symbols Deconvolution TA1-2040 Random variable Algorithm Degradation (telecommunications) |
| Zdroj: | Applied Sciences Volume 11 Issue 9 Applied Sciences, Vol 11, Iss 4133, p 4133 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app11094133 |
| Popis: | In most degradation tests, the measuring processes is affected by several conditions that may cause variation in the observed measures. As the measuring process is inherent to the degradation testing, it is important to establish schemes that define a certain level of permissible measurement error such that a robust reliability estimation can be obtained. In this article, an approach to deal with measurement error in degradation processes is proposed, the method focuses on studying the effect of such error in the reliability assessment. This approach considers that the true degradation is a function of the observed degradation and the measurement error. As the true degradation is not directly observed it is proposed to obtain an estimate based on a deconvolution operation, which considers the subtraction of random variables such as the observed degradation and the measurement error. Given that the true degradation is free of measurement error, the first-passage time distribution will be different from the observed degradation. For the establishment of a control mechanism, these two distributions are compared using different indices, which account to describe the differences between the observed and true degradation. By defining critical levels of these indices, the reliability assessment may be obtained under a known level of measurement error. An illustrative example based on a fatigue-crack growth dataset is presented to illustrate the applicability of the proposed scheme, the reliability assessment is developed, and some important insights are provided. |
| Databáze: | OpenAIRE |
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