International Journal of Reliability, Quality and Safety Engineering

In this paper, we study some reliability aspects of proportional reversed hazards model (PRHM) using quantile functions and derive certain characteristic properties. A quantile-based relevation transform for past lifetime random variable is introduced. The relationships between quantile-based PRHM and quantile versions of measures of divergence are investigated.

Fault prognosis in industrial plants is a complex problem, and time is an important factor for the resolution of this problem. The main indicator for the task of fault prognosis is the estimate of remaining useful life (RUL), which essentially depends on the predicted time to failure. This paper introduces a temporal neuro-fuzzy system (TNFS) for performing the fault prognosis task and exactly estimating the RUL of preheater cyclones in a cement plant. The main component of the TNFS is a set of temporal fuzzy rules that have been chosen for their ability to explain the behavior of the entire system, the components’ degradation, and the RUL estimation. The benefit of introducing time in the structure of fuzzy rules is that a local memory of the TNFS is created to capture the dynamics of the prognostic task. More precisely, the paper emphasizes improving the performance of TNFSs for prediction. The RUL estimation process is broken down into four generic processes: building a predictive model, selecting the most critical parameters, training the TNFS, and predicting RUL through the generated temporal fuzzy rules. Finally, the performance of the proposed TNFS is evaluated using a real preheater cement cyclone dataset. The results show that our TNFS produces better results than classical neuro-fuzzy systems and neural networks.

The importance of reliability to complex systems cannot be disputed as they are the backbones of our society. In practice, the common cause failures may have severe reverse function on complex systems’ overall stability. Survival Signature opens a new way to perform reliability analysis on systems with multiple component types. This paper under takes a research on survival signature-based reliability analysis on complex systems susceptible to Common Cause Failures. To be specific, it proposes the standard $\alpha$-factor model and general $\alpha$-factor model to combine with the survival signature. In practical applications, the $\alpha$-factor estimator of the system might not be defined completely due to limited data, or knowledge which requires to take imprecision into account. Some numerical cases are presented to show the applicability of the methods for complex systems. In addition, this paper may attract people’s attention on the conception of Design for Reliability.

Time redundancy is a method of increasing the reliability and efficiency of the operation of systems for various purposes. A system with time redundancy provides additional time (a time reserve) for restoring characteristics and synchronizing the work of its individual elements. Time reservation is used in production, energy, gas transportation, information, ergonomic systems and some others. In this paper, based on the theory of semi-Markov processes with a common phase space of states, a semi-Markov model of a multicomponent system with a group instantly replenished time reserve is constructed. The reliability characteristics of the system are determined. To approximate the probability and the average time of failure-free operation of the system in conditions of high reliability, asymptotic phase-merging scheme algorithms are used.

In the face of fierce global competition, firms are outsourcing important but nonessential tasks to external professional companies. Corporations are also turning from competitive business models to cooperative strategic partnerships in hopes of swiftly responding to consumer needs and enhancing overall efficiency and industry competitiveness. This research developed an outsourcing partner selection model in hopes of helping firms select better outsourcing partners for long-term collaborations. Process quality and manufacturing time are vital when evaluating outsourcing partner. We therefore used process capability index $C_{pm}$ and manufacturing time performance index $I_h$ in the proposed model. Sample data from random samples are needed to calculate the point estimates of indices, however, it is impossible to obtain a sample with a structure completely identical to that of the population, which means that sampling generates unavoidable sampling errors. The reliability of point estimates are also uncertain, which inevitably leads to misjudgment in some cases. Thus, to reduce estimate errors and increase assessment reliability, we calculated the $100(1-\alpha )$% confidence intervals of the indices $C_{pm}$ and $I_h$, then constructed the joint confidence region of $C_{pm}$ and $I_h$ to develop an outsourcing partner selection model that will help firms select better outsourcing partners for long-term collaborations. We also provide a case as an illustration of how the proposed selection model is implemented.