In daily life, many products, such as light bulbs, fuses, dry batteries, fireworks, semiconductors, are non‐repairable. The non‐repairable products are usually referred to as one‐shot products, or as failed products that are not worth repairing. A one‐shot product is usually required to perform a function once only since its use is normally accompanied by an irreversible reaction or process, e.g. chemical reaction or physical destruction. However, most one‐shot products being stored or deployed are usually not under continuous surveillance. The failed products can only be found by inspection or at the beginning of operation. Therefore, this paper seeks to assess the reliability of one‐shot products.
The study considers a series system consisting of m components with lifetime following Weibull distribution, and applies a competing failure model to investigate the proposed series system for one‐shot products. The maximum likelihood estimators (MLEs) of parameters of the Weibull distribution based on the quantal‐response data in the proposed series system are derived. The model is illustrated with a two‐component series system, and the statistical properties of the MLEs are investigated by Monte Carlo simulation under the two‐stage inspection scheme and the three‐stage inspection scheme.
Simulation results reveal not only that the MLEs of Weibull parameters gradually approximate the true values of Weibull parameters under rising sample sizes, but also that the precision and accuracy of the MLEs of parameters increase with an increasing sample size. Furthermore, the standard deviations of MLEs of Weibull parameters for the two‐stage inspection scheme are smaller than those for the three‐stage inspection scheme.
The paper focuses on the reliability assessment of one‐shot products, e.g. firework, ammunition, airbag, injector, dry battery, with Weibull components lifetime distribution.
Pan, C. and Chu, L. (2010), "Reliability assessment for one‐shot product with Weibull lifetime components", International Journal of Quality & Reliability Management, Vol. 27 No. 5, pp. 596-610. https://doi.org/10.1108/02656711011043553Download as .RIS
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