International Journal of Reliability, Quality and Safety Engineering

A hybrid energy system integrates renewable energy sources like wind, solar, micro-hydro and biomass, fossil fuel power generators such as diesel generators and energy storage. Hybrid energy system is an excellent option for providing electricity for remote and rural locations where access to grid is not feasible or economical. Reliability and cost-effectiveness are the two most important objectives when designing a hybrid energy system. One challenge is that the existing methods do not consider the time-varying characteristics of the renewable sources and the energy demand over a year, while the distributions of a power source or demand are different over the period, and multiple power sources can often times complement one another. In this paper, a reliability analysis method is developed to address this challenge, where wind and solar are the two renewable energy sources that are considered. The cost evaluation of hybrid energy systems is presented. A numerical example is used to demonstrate the proposed method.

Degradation analysis is used to analyze the useful lifetimes of systems, their failure rates, and various other system parameters like mean time to failure (MTTF), mean time between failures (MTBF), and the system failure rate (SFR). In many systems, certain possible parallel paths of execution that have greater chances of success are preferred over others. Thus we introduce here the concept of probabilistic parallel choice. We use binary and n-ary probabilistic choice operators in describing the selections of parallel paths. These binary and n-ary probabilistic choice operators are considered so as to represent the complete system (described as a series-parallel system) in terms of the probabilities of selection of parallel paths and their relevant parameters. Our approach allows us to derive new and generalized formulae for system parameters like MTTF, MTBF, and SFR. We use a generalized exponential distribution, allowing distinct installation times for individual components, and use this model to derive expressions for such system parameters.

Compared to traditional vehicles, the Fuel Cell Vehicle (FCV) is more energy efficient and emission free. As an ideal solution to global energy crisis, FCV has aroused widespread attention in China and abroad. However, the complex structure of FCV and the weak foundation for manufacturing which hinder the improvement of FCV's reliability. In this paper, a statistical analysis that characterizes the reliability of FCV based on field test data is presented. The failure distribution rules and failure reasons of the entire FCV and its subsystems are discussed. Meanwhile, the least squares estimation — correlation coefficient inspection method is proposed to find out the distribution model of the Mileage to First Failure (MFF) and the Mileage between Failures (MBF) for the entire FCV and its subsystems. It is found that they all follow Weibull distribution. Based on the proposed methods and analysis results, the general direction with respect to technological breakthrough is pointed out to make the entire FCV and its subsystems more reliable and durable.

As an airframe has finite lifetime and has to be designed lightweight, the maintenance of airframe is indispensable to operate aircraft without any serious troubles. After an airframe begins to operate, it suffers stresses and the stress causes the damage such as cracks of the airframe. Cracks grow with operation time and cause catastrophic phenomenon such as the mid-air disintegration when they become greater than a critical size. So, the managerial crack size is prespecified and Preventive Maintenance (PM) undergoes when the inspected crack size exceeds it. In this paper, optimal PM policies of airframe crack failure are discussed. Airframe states are represented as the Markov renewal process, and one-step transition probabilities are discussed. The total expected cost from the start of operation to the end by failure is defined and the optimal PM policies which minimize it is discussed.

Recently many popular reliability systems are being extended to weighted systems where weight associated with each component refers to load/capacity of that component. The simplest k-out-of-n system extended under this concept is known as weighted-k-out-of-n system. A weighted-k-out-of-n: G(F) system consists of n components each one having a positive integer weight w_i, i = 1, 2, …, n such that the total system weight is w and the system works (fails) if and only if the accumulated weight of working (failed) components is at least k. In this paper, we introduce the concept of Weighted Markov Bernoulli Trial (WMBT) {X_i, i ≥ 0} and study the distribution of , the total weight of successes in the sequence of n WMBT and conditional distribution of given that uth trial results in failure/success. We refer the distribution of as Weighted Markov Binomial Distribution (WMBD). Further we discuss application of WMBD and conditional WMBD in evaluation of reliability, Birnbaum reliability importance (B-importance) and improvement potential importance of weighted-k-out-of-n: G/F system. The numerical work is included to demonstrate the computational simplicity of the developed results. Further we compare our study with the existing results in terms of efficiency and find that our results are efficient for large values of k.