International Journal of Reliability, Quality and Safety Engineering

In this paper, we attempt generalization of the Markovian software reliability model (MSRM). Defining the stochastic process whose state space is the cumulative number of corrected faults, we show the theoretical framework of the MSRM by assuming that the time interval between software failures is distributed generally. We also consider the imperfect debugging environment where the debugging activities are uncertain. Several software reliability assessment measures are expressed with the distribution of the transition time between arbitrary two states. Furthermore, we propose the approximation method for practical computation of the quantitative measures. Finally, we investigate the validity of our proposed approximation method.

Resistance welding is to bond two pieces of metal materials together by melting the materials in the contact area. An elevated temperature is generated by contact resistance when electrical current flows through the contact area of the two materials. Generally, a current is applied through two close electrodes. Sometimes, however, it is necessary to set the two electrodes far apart when the contact area is physically unreachable. The far separation of the electrodes makes difficult to generate reliable welds. This paper describes a reliability and robustness design method for the welding process. In particular, a concept design was conducted to avoid welding failure modes. The critical dimensions of a work piece were defined to make a successful weld. Then the welding processes for various dimensions of the work pieces were optimized using Taguchi's parameter design method. A thermal cycle test was performed to evaluate the reliability of the welds. It is shown that the welding process is robust and the welds have high reliability.

This paper identifies factors that impact reliability and safety of ocean turbines. We describe how physical and environmental factors will impact the design of its machine condition monitoring (MCM) system. Environmental factors like fouling, corrosion, and inaccessibility of equipment sets this MCM problem apart from those encountered by wind turbines, hydroelectric plants, or even ship hulls and propellers. Fouling constitutes the primary and most persistent source of failure. In addition to compromising turbine efficiency and reliability, fouling reduces sensor data quality — masking faults that will ultimately lead to failure. Unmitigated fouling triggers a form of biological succession known as flocculation that may eventually attract threatened species of tortoises and cetaceans to this rotating machinery. We review and suggest refinements to a class of non-toxic biologically-inspired anti-fouling techniques known as engineered topographies. Advances in this area will enable turbines to operate in portions of the water column that maximize momentum flux while minimizing retrieval cost.

In recent years, software development has become more large-scaled, complicated, and diversified. At the same time, customer requirement of high quality and shortened delivery has increased. Therefore, we have to manage process quality and control product quality in the early-stage of software development in order to produce highly quality software products during the limited period. In this paper, we conduct multivariate linear analyses by using process monitoring data, derive effective process factors affecting the final product quality, and discuss the significant process factors with respect to software management measures of quality, cost, and delivery (QCD). Then, we discuss project management on the significant process factors affecting QCD and show its effect on QCD.

Most of the existing research in multi-state systems relies on point estimation for modeling and optimization. The assessment of uncertainty during design is essential, yet variability in system performance is commonly ignored. Unfortunately, unlimited testing which could provide these arbitrarily accurate estimates is not economical. This paper describes a statistical inference technique to quantify the uncertainties inherent in limited testing. The methodology enables estimation of joint confidence intervals for both system and component performance distributions and subsequently provides a hypothesis testing procedure to perform objective assessments. This builds on previous research which has only addressed confidence bounds for system reliability. Instead of dichotomizing systems into acceptable and unacceptable classes, our approach can handle the case when a system exhibits three or more distinct performance levels. Thus, the method does not place restrictions on the flexibility of the underlying multi-state system concept. The value of the approach is illustrated using a case study and several experiments. The results indicate that joint confidence intervals produced by this procedure are accurate for a range of common confidence levels and sample sizes. It is also demonstrated how hypothesis testing and uncertainty assessment may be used to objectively measure system readiness.