Prioritizing system-reliability prediction improvements

This method prioritizes system-reliability prediction activities once a preliminary reliability-prediction has been made. System-reliability predictions often use data and models from a variety of sources, each with differing degrees of estimation uncertainty. Since time and budgetary constraints limit the extent of analyzes and testing needed to estimate component reliability, it is necessary to allocate limited resources intelligently. A reliability-prediction prioritization index (RPPI) is defined to provide a relative ranking of components based on their potential for improving the accuracy of a system-level reliability prediction by decreasing the variance of the system-reliability estimate. If a component has a high RPPI, then additional testing or analysis should be considered to decrease the variance of the component reliability estimate. RPPI is based on a decomposition of the variance of the system-reliability or on a mean-time-to-failure estimate. Using these indexes, the effect of individual components within the system can be compared, ranked, and assigned to priority groups. The ranking is based on whether a decrease of the component-reliability estimate variance meaningfully decreases the system-reliability estimate variance. The procedure is demonstrated with two examples     

Subcutaneously implanted central venous access devices in cancer patients: a prospective analysis

BACKGROUND: Long term intravenous access is a common requirement for cancer patients. This analysis was designed to determine device-related morbidity and factors predictive of poor long term outcome for patients with subcutaneous single lumen intravenous access ports. METHODS: Six hundred eighty patients who underwent subcutaneous intravenous port placement between June 1987 and May 1989 at Memorial Sloan-Kettering Cancer Center were followed prospectively until port removal, death, or a maximum of 1960 days. Indications for and circumstances of placement, patient diagnoses, patient demographics, and subsequent courses of treatment were recorded, as well as technical and microbiologic device-related complications. Total, device specific, and complication free device durations were calculated. RESULTS: The median patient age was 52.4 years (range, 1.6-83.9 years). The female-to-male ratio was 1.5 to 1. Cancer diagnoses included solid organ tumors (84%), leukemia (4%), lymphoma (11%), and others (1%). Indications included access for systemic chemotherapy (98%), total parenteral nutrition (0.5%), and others (1.5%). One insertion complication and six insertion failures occurred, without mortality. The estimated mean overall actuarial device specific duration was 1191 days (range, 2-1960 days). Actuarial mean complication free, device specific duration was 952 days. Complications included sepsis (n = 31; 4.4%), site infection (n = 31; 4.4%), and accessibility failures such as thrombosis and leakage (n = 40, 5.7%). Reasons for end of port duration were patient death (72.4%), end of treatment (13.5%), functional failure or intractable infection (11.2%), and others (2.9%). Independent factors correlating with decreased port specific, complication free duration included placement site, age, tumor type, and catheter tip position. CONCLUSIONS: Subcutaneous intravenous access ports in cancer patients are safe and well tolerated. Long term device duration is primarily influenced by patient survival. In this study, 90% of patients alive at 1 year and 70% of patients alive at 4 years had a functional port.     

Composite importance measures for multi-state systems with multi-state components

This paper presents & evaluates composite importance measures (CIM) for multi-state systems with multi-state components (MSMC). Importance measures are important tools to evaluate & rank the impact of individual components within a system. For multi-state systems, previously developed measures do not meet all user needs. The major focus of the study is to distinguish between two types of importance measures which can be used for evaluating the criticality of components in MSMC with respect to multi-state system reliability. This paper presents Type 1 importance measures that are involved in measuring how a specific component affects multi-state system reliability. A Monte Carlo (MC) simulation methodology for estimating the reliability of a MSMC is used for computing the proposed CIM metrics. Previous approaches (Type 2) have focused on investigating how a particular component state or set of states affects multi-state system reliability. For some systems, it is not clear how to prioritize system component importance, collectively considering all of its states, using the previously developed importance measures. That detracts from those measures. Experimental results show that the proposed CIM can be used as an effective tool to assess component criticality for MSMC. Examples are used to illustrate & compare the proposed CIM with previous multi-state importance measures.     

Reliability optimization of series-parallel systems using a genetic algorithm

A problem-specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems. The problem is to select components and redundancy-levels to optimize some objective function, given system-level constraints on reliability, cost, and/or weight. Previous formulations of the problem have implicit restrictions concerning the type of redundancy allowed, the number of available component choices, and whether mixing of components is allowed. GA is a robust evolutionary optimization search technique with very few restrictions concerning the type or size of the design problem. The solution approach was to solve the dual of a nonlinear optimization problem by using a dynamic penalty function. GA performs very well on two types of problems: (1) redundancy allocation originally proposed by Fyffe, Hines, Lee, and (2) randomly generated problem with more complex k-out-of-n:G configurations.     

Gamma distribution parameter estimation for field reliability data with missing failure times

Maximum likelihood estimators have been developed for the gamma distribution when there is missing time-to-failure information. Data sets with missing time-to-failure data can arise from field data collection systems that rely on recorded observations of the system by the operators and maintenance personnel. In many regards, this type of data is highly desirable because it implicitly accounts for all actual usage and environmental stresses. Unfortunately the component times-to-failure are not always recorded for fielded systems because of a lack of elapsed time meters, unsatisfactory data reporting requirements, or incomplete or lost information. When only data of this type is available, it creates a non-standard form of da'ta censoring and it has generally not been possible to fit most common time-to-failure distributions. Reliability practitioners have sometimes made unsubstantiated simplifying assumptions so the data can be used. In this paper, a more rigorous approach is presented. Maximum likelihood estimators are derived and demonstrated for the gamma distribution based on merged data records where the individual failure times have not been recorded. These results are important because the gamma distribution can model diverse time-to-failure behavior. This provides a particularly useful tool for data sets that may otherwise not be satisfactorily analyzed.     

Efficiently Solving the Redundancy Allocation Problem Using Tabu Search

A tabu search meta-heuristic has been developed and successfully demonstrated to provide solutions to the system reliability optimization problem of redundancy allocation. Tabu search is particularly well-suited to this problem and it offers distinct advantages compared to alternative optimization methods. While there are many forms of the problem, the redundancy allocation problem generally involves the selection of components and redundancy levels to maximize system reliability given various system-level constraints. This is a common and extensively studied problem involving system design, reliability engineering and operations research. It is becoming increasingly important to develop efficient solutions to this reliability optimization problem because many telecommunications (and other) systems are becoming more complex, yet with short development schedules and very stringent reliability requirements. Tabu search can be applied to a more diverse problem domain compared to mathematical programming methods, yet offers the potential of greater efficiency compared to population-based search methodologies, such as genetic algorithms. The tabu search is demonstrated on numerous variations of three different problems and compared to integer programming and genetic algorithm solutions. The results demonstrate the benefits of tabu search for solving this type of problem.     

A heuristic for solving the redundancy allocation problem for multi-state series-parallel systems

The redundancy allocation problem is formulated with the objective of minimizing design cost, when the system exhibits a multi-state reliability behavior, given system-level performance constraints. When the multi-state nature of the system is considered, traditional solution methodologies are no longer valid. This study considers a multi-state series-parallel system (MSPS) with capacitated binary components that can provide different multi-state system performance levels. The different demand levels, which must be supplied during the system-operating period, result in the multi-state nature of the system. The new solution methodology offers several distinct benefits compared to traditional formulations of the MSPS redundancy allocation problem. For some systems, recognizing that different component versions yield different system performance is critical so that the overall system reliability estimation and associated design models the true system reliability behavior more realistically. The MSPS design problem, solved in this study, has been previously analyzed using genetic algorithms (GAs) and the universal generating function. The specific problem being addressed is one where there are multiple component choices, but once a component selection is made, only the same component type can be used to provide redundancy. This is the first time that the MSPS design problem has been addressed without using GAs. The heuristic offers more efficient and straightforward analyses. Solutions to three different problem types are obtained illustrating the simplicity and ease of application of the heuristic without compromising the intended optimization needs.     

Redundancy allocation for series-parallel systems using a max-min approach

The redundancy allocation problem is formulated with the objective of maximizing the minimum subsystem reliability for a series-parallel system. This is a new problem formulation that offers several distinct benefits compared to traditional problem formulations. Since time-to-failure of the system is dictated by the minimum subsystem time-to-failure, a logical design strategy is to increase the minimum subsystem reliability as high as possible, given constraints on the system. For some system design problems, a preferred design objective may be to maximize the minimum subsystem reliability. Additionally, the max-min formulation can serve as a useful and efficient surrogate for optimization problems to maximize system reliability. This is accomplished by sequentially solving a series of max-min subproblems by fixing the minimum subsystem reliability to create a new problem. For this new formulation, it becomes possible to linearize the problem and use integer programming methods to determine system design configurations that allow mixing of functionally equivalent component types within a subsystem. This is the first time the mixing of component types has been addressed using integer programming. The methodology is demonstrated on three problems.     

A degradation-based model for joint optimization of burn-in, quality inspection, and maintenance: a light display device application

This paper presents a degradation-based model to jointly determine the optimal burn-in, inspection, and maintenance decisions, based on degradation analysis and an integrated quality and reliability cost model. Degradation modeling plays an important role in reliability prediction and analysis for many highly reliable components and equipment, when the failures can rarely be observed. Unlike traditional applications, quality and reliability must be considered simultaneously for devices subject to degradation, because quality inspection decisions often impact anticipated reliability and failure-time distributions. This paper presents an integrated model to jointly optimize quality and reliability for devices subject to degradation, with a focus on burn-in, quality inspection, and maintenance policies. Based on the degradation modeling and analysis, the reliability function and the time-to-failure distribution are derived under the condition that the quality inspection is applied following the burn-in period. The optimal burn-in, quality inspection, and preventive maintenance policies are determined by minimizing the expected total cost per usage lifetime. The proposed model is illustrated using the application of light display devices, in which the degradation path follows a negative shifted lognormal distribution with a random failure threshold. A numerical example is provided to illustrate the application of our model to the light display devices.