In this paper we apply statistical reliability tools to manage and seek improvements in the strengths of medium density fiberboard (MDF). As a part of the MDF manufacturing process, the product undergoes destructive testing at various intervals to determine compliance with customer′s specifications. Workers perform these tests over sampled cross sections of the MDF panel to measure the internal bond (IB) in pounds per square inches until failure. We explore both graphically and statistically this "pressure-to-failure" of MDF. Also, we briefly comment on reducing sources of variability in the IB of MDF.

This paper proposes a U-test statistic for the problem of testing that a life distribution is exponential against the alternative that it is harmonic new better (worse) than used in expectation upper tail HNBUET (HNWUET), but not exponential on complete data. Selected critical values are tabulated for sample sizes n =5(1)60. The asymptotic normality of the statistic is proved and a comparison is made of the asymptotic efficiency between the statistic and other statistics. The power of the test is studied by simulation. A test for HNBUET in the case of randomly right-censored data is also considered. An application of the proposed test statistic in medical sciences is given.

Rail breaks and derailments can cause a huge loss to rail players due to loss of service, revenue, property or even life. Maintenance has huge impact on reliability and safety of railroads. It is important to identify factors behind rail degradation and their risks associated with rail breaks and derailments. Development of mathematical models is essential for prediction and prevention of risks due to rail and wheel set damages, rail breaks and derailments. This paper addresses identification of hazard modes, estimation of probability of those hazards under operating, curve and environmental condition, probability of detection of potential hazards before happening and severity of those hazards for informed strategic decisions. Emphasis is put on optimal maintenance and operational decisions. Real life data is used for illustration.

In the present work, a moment inequality is derived for new renewal better (worse) than used in expectation(NRBUE) (NRWUE) distributions. This inequality demonstrates that if the mean life is finite then all higher order moments exist. A new test statistics for testing exponentiality against NRBUE (NRWUE) is introduced based on this inequality. It is shown that the proposed test is simple and has high relative efficiency for some commonly used alternatives. Critical values are tabulated for sample sizes n = 5(1)30. A set of real data is used as an example to elucidate the use of the proposed test statistics for practical reliability analysis.