International Journal of Reliability, Quality and Safety Engineering

This paper presents a data-driven, similarity-based approach for prognostics of industrial and structural components. The potentiality of the approach is demonstrated on a problem of crack propagation, taken from literature. The crack growth process is described by a nonlinear model affected by nonadditive noises. A comparison is provided with an existing Monte Carlo-based estimation method, known as particle filtering.

The safety evaluations of structures are usually performed by assuming uncertain 18 input parameters either as probabilistic or possibilistic in nature. Safety evaluations of structural systems characterized by some of the input parameters as possibilistic and others that are sufficient enough to model as probabilistic are unsuitable and restricted to gross assumption at early stages of modeling. The present paper deals with the safety analysis of such a hybrid uncertain system. Relying on the fundamental concept of entropy-based transformation; the feasibility of reliability analysis of a hybrid uncertain system in a possibilistic format is demonstrated in the present study. This will enable the designers to model the structural parameter uncertainty probabilistically as well possibilistically depending on the nature and quality of the input data. The bounds on the probability of failure based on the evidence theory are also computed to study the consistency of the possibility of failure results obtained by the transformation-based algorithm. A numerical example illustrates the capability of the proposed possibilistic approach of safety evaluation of hybrid uncertain system.

In this paper, reliability analysis of thermocouple, RTD, and thermistor temperature sensing technologies and their corresponding systems used for controlling the main systems in many critical applications are carried out. Failure mode and effect analysis (FMEA) and the failure rate prediction of each component in the three temperature sensor systems are carried out and presented. All the major and critical failure modes and their respective corrective actions for preventing the same are established. This will help the designer to eliminate the occurrence of such failure modes by redesigning/design improvement. The concept of Pareto analysis is applied to the three temperature sensor systems for the failure rate analysis and prioritization for improvement of high failure rate components.

The intensity function is the key entity to the power law process, also known as the Weibull process or nonhomogeneous Poisson process. It gives the rate of change of the reliability of a system as a function of time. We illustrate that a Bayesian analysis is applicable to the power law process through the intensity function. First, we show using real data, that one of the two parameters in the intensity function behaves as a random variable. With a sequence of estimates of the subject parameter we proceeded to identify the probability distribution that characterizes its behavior. Using the commonly used squared-error loss function we obtain a Bayesian reliability estimate of the power law process. Also a simulation procedure shows the superiority of the Bayesian estimate with respect to the maximum likelihood estimate and the better performance of the proposed estimate with respect to its maximum likelihood counterpart. As well, it was found that the Bayesian estimate is sensitive to a prior selection.

Safety instrumented systems (SISs) are installed to provide risk reduction and the performance of a SIS can be assessed by its ability to reduce risk. This article introduces a new quantitative measure for the risk reduction, denoted PFD*. Compared with the current reliability measures, the new measure takes into account the demand rate, and therefore can be used for SISs operating in both low-demand and high-demand mode. For a SIS operating in low-demand mode, the PFD* is approximately equal to the standard probability of failure on demand (PFD) used in IEC 61508 and related standards. PFD* can therefore be considered as an extension and improvement of the standard PFD. Successful handling of a demand verifies the functional status of a SIS in a way similar to a functional test, and the PFD* will therefore depend on the demand rate. The PFD* can be used to select the functional test interval according to the risk reduction allocated to the specific SIS. The properties of the new measure are analyzed through a case study of a 1-out-of-2 system of pressure transmitters.