IEEE Transactions on Reliability· 2013Q1
Degradation Data Analysis Using Wiener Processes With Measurement Errors
- 364citations
- Q1SCImago
- 2013year
Short summary
A new mixed-effects model incorporating measurement errors improves the estimation efficiency of Wiener processes for system degradation analysis, outperforming traditional methods.
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Key points
- Introduces a mixed-effects Wiener process model to analyze degradation data with measurement errors.
- Improves estimation efficiency over traditional Wiener process inference methods.
- The model can account for heterogeneity within a population.
- Demonstrates applicability to HDD magnetic head wear and LED light intensity degradation.
AI-generated from the title and abstract; the full text is not read.
Abstract
Degradation signals that reflect a system's health state are important for diagnostics and health management of complex systems. However, degradation signals are often compounded and contaminated by measurement errors, making data analysis a difficult task. Motivated by the wear problem of magnetic heads used in hard disk drives (HDDs), this paper investigates Wiener processes with measurement errors. We explore the traditional Wiener process with positive drifts compounded with i.i.d. Gaussian noises, and improve its estimation efficiency compared with the existing inference procedure. Furthermore, to capture the possible heterogeneity in a population, we develop a mixed effects model with measurement errors. Statistical inferences of this model are discussed. The mixed effects model subsumes several existing Wiener processes as its limiting cases, and thus it is useful for suggesting an appropriate Wiener process model for a specific dataset. The developed methodologies are then applied to the wear problem of magnetic heads of HDDs, and a light intensity degradation problem of light-emitting diodes.
The authors' abstract, as published at the source. IEEE Transactions on Reliability, 2013 · DOI ↗
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Field: Safety, Risk, Reliability and Quality
Safety, Risk, Reliability and QualityEngineering