Reliability Growth Analysis Research Articles

On general uncertainty data and general uncertainty variable for reliability growth analysis of major aerospace equipment

PurposeThe purpose of this paper is to explore new ways and lay a solid foundation to solve the problem of reliability growth analysis of major aerospace equipment with various uncertainty data through propose new concepts of general uncertainty data (GUD) and general uncertainty variable (GUV) and build the operation system of GUVs.Design/methodology/approachThe characteristics of reliability growth data of major aerospace equipment and the limitations of current reliability growth models have been analyzed at first. The most commonly used uncertainty system analysis methods of probability statistics, fuzzy mathematics, grey system theory and rough set theory have been introduced. The concepts of GUD and GUV for reliability growth data analysis of major aerospace equipment are proposed. The simplified form of GUV based on the “kernel” and the degree of uncertainty of GUV is defined. Then an operation system of GUVs is built.Findings(1) The concept of GUD; (2) the concept of GUV; (3) The novel operation rules of GUVs with simplified form.Practical implicationsThe method exposed in this paper can be used to integrate complex reliability growth data of major aerospace equipment. The reliability growth models based on GUV can be built for reliability growth evaluation and forecasting of major aerospace equipment in practice. The reliability evaluation example of a solid rocket motor shows that the concept and idea proposed in this paper are feasible. The research of this paper opens up a new way for the analysis of complex uncertainty data of reliability growth of major aerospace equipment. Moreover, the operation of GUVs could be extended to the case of algebraic equation, differential equation and matrix which including GUVs.Originality/valueThe new concepts of GUD and GUV are given for the first time. The novel operation rules of GUVs with simplified form were constructed.

Retail Hydrogen Station Reliability Status and Advances

Abstract One of the most important emerging commercial markets for hydrogen is fuel cell-powered mobility including cars, trucks, and buses. These vehicles are refueled via a network of hydrogen fueling stations, with the highest number of U.S. stations being in California. The numbers of both fuel cell electric vehicles (FCEVs) and hydrogen stations have increased in the last two years, with anecdotal information from FCEV drivers indicating that station reliability is hurting the consumer acceptability of FCEV technologies. Therefore, this study benchmarks the current state of hydrogen station reliability in practice and presents on-going research that is investigating the failures that are contribute to hydrogen station reliability issues. This is accomplished with an analysis of operation, safety, and maintenance data from hydrogen stations and fuel cell electric vehicles to benchmark the maintenance and failure of hydrogen stations and their components. This analysis, of over 5,000 station maintenance events, presents the leading maintenance categories and failure rates, and is a prerequisite to the development of data-driven reliability improvement plans. We present a reliability growth analysis and on-going research into the root causes of failure for dispensers, a particularly failure-prone subsystem.

Criticality-based reliability-centered maintenance for healthcare

PurposeMedical technologies and assets are one of the main drivers of increasing healthcare cost. The rising number and complexity of medical equipment have forced hospitals to set up and regulate medical equipment management programs to ensure critical devices are safe and reliable. The purpose of this paper is to gain insights into maintenance management-related activities for medical equipment. The paper proposes applying a tailored reliability-centered maintenance (RCM) approach for maintenance activities selection for medical equipment. Such approach will support assets management teams in enhancing operation, decrease risk and cost, and ultimately improve health of patients served by these equipment.Design/methodology/approachThe traditional RCM approach will be used with a focus on criticality reduction. By criticality, the authors refer to the severity of failures and occurrence. The proposed method relies on the use of reliability growth analysis for opportunity identification followed by a thorough failure mode and effect analysis to investigate major failure modes and propose ways to reduce criticality. The effectiveness of the proposed method will be demonstrated using a case of one of the leading obstetric and gynecological hospitals in United Arab Emirates and in the Gulf Cooperation Council region.FindingsThe case examines the relationship between the current practice of planned preventive maintenance and the failure rates of the equipment during its life span. Although a rigorous preventive maintenance program is implemented in the hospital under study, some critical equipment show an increasing failure rates. The analysis highlights the inability of traditional time-driven preventive maintenance alone in preventing failures. Thus, a systematic RCM approach focused on criticality is more beneficial and more time and cost effective than traditional time-driven preventive maintenance practices.Practical implicationsThe study highlights the need for utilizing RCM approach with criticality as the most important prioritization criterion in healthcare. A proper RCM implementation will decrease criticality and minimize the risk of failure, accidents and possible loss of life. In addition to that, it will increase the availability of equipment, and reduce cost and time.Originality/valueThis paper proposes a maintenance methodology that can help healthcare management to improve availability and decrease the risk of critical medical equipment failures. Current practices in healthcare facilities have difficulty identifying the optimal maintenance strategy. Literature focused on medical maintenance approach selection is rather limited, and this paper will help in this discussion. In addition to that, the Association for the Advancement of Medical Instrumentation supports the initiative of adopting RCM on a large scale in healthcare. Therefore, this paper address the gap in the literature for medical equipment maintenance and the work is in line with the recommendation of leading healthcare association. The paper also presents statistical review of the total number of received maintenance work orders during one full year in the hospital under study. The analysis supports the need for more research to examine current practice and propose more effective maintenance approaches.

System Reliability Growth Analysis during Warranty

This paper presents system reliability growth analysis using actual field failure data. The primary objective of the system reliability growth is to improve the achievement of system reliability performance during system reliability demonstration, in order to achieve the predicted or contractually required system reliability commitment. An effective reliability growth model can be utilized to predict when the reliability target can be achieved based on previous reliability performance. In this paper, the system reliability growth analysis is illustrated using the Duane and AMSAA reliability growth models to determine applicability and aid in choice determination. The Duane model is a better choice for failure terminated reliability growth while AMSAA is a better choice for time terminated reliability growth. Comparisons of the Duane versus AMSAA model are carried out by conducting the statistical analysis on the observed field failures.

A Case Study on Reliability Growth Analysis for a missile System composed of All-Up-Round Missile and Launcher

A Case Study on Reliability Growth Analysis for a missile System composed of All-Up-Round Missile and Launcher

A Reliability-Based Strategy for the Analysis of Single Proton Exchange Membrane Fuel Cells

The development of power conversion systems based on fuel cells has been demanding reliability studies since the requirements associated with cost and durability of these technological products have become fundamental to their acceptance by the energy market. The experimental part of the reliability study presented in this work consisted of performing life tests with single proton exchange membrane fuel cells (PEMFCs). The proposed reliability analysis methodology covered the application of qualitative and quantitative techniques. In the qualitative approach, a Failure Mode and Effect Analysis was developed in order to identify and evaluate all potential failures associated with the operation of fuel cells. In the quantitative approach, a statistical analysis was applied to the sample data generated in long-term steady-state tests of these devices. A two-parameter exponential distribution was fitted to data and the maximum likelihood estimate for the mean time to failure (MTTF) of the fuel cells was calculated. It is important to point out that the tests performed under the scope of this study were the first long-term experiments performed with the fuel cells produced in the laboratories of IPEN-CNEN/SP, Brazil. Although the results indicated that fuel cell performance and durability were still at a level below the targets normally established for similar commercial devices, the improvement of the main components of PEMFCs has been the objective of several projects developed at the institute. Thus, the main benefit brought by this study is the proposed methodology, which can be implemented as part of a reliability growth analysis of the fuel cells and can be integrated into the design process of these devices.

Evaluation of a grid-independent solar photovoltaic system for primary health centres (PHCs) in developing countries

This paper presents the evaluation of a stand-alone solar photovoltaic (PV) electricity supply system for rural primary health centres (PHCs) in developing countries, using a PHC at Abadam local government area in North-West Nigeria as a case study. The paper is guided by standards for sizing and estimating the performance of stand-alone PV/battery systems. The research presents PV/battery/inverter capacity, yield and losses, battery state of charge, reliability and load growth analysis. The economic analysis is based on the life cycle cost assessment approach, while the environmental evaluation is considered in terms of the quantity of diesel fuel and carbon emissions avoided by the proposed solar PV system. A PV system capacity of 3.6–6 kW is designed for the clinic's total daily demand plus 25% load growth, with a yield of 4.6 h/d. This energy system can achieve a high availability over the year. The initial, life cycle, O and M and the replacement costs range from $ 14,356 to $ 14,506, $ 17,909 to $ 18, 080, $ 1534 and $ 4451 to $ 4498, while the cost of energy ranges from $ 0.239 to $ 0.295/kWh. The diesel system which has an initial cost of $ 451 has a relatively higher life cycle cost of $ 38,135–$ 40,460. The proposed PV system has the potential to avoid 8357–8956 kg/year of CO2 after implementation.

Investigations on Reliability Growth for High-Speed Punch Based on Whole Life Cycle

This paper presents a study on the reliability growth analysis of high-speed punch. The main contribution of the paper is the development of an efficient approach to analyze the reliability growth and to make reliability growth management of high-speed punch. The reliability growth methods for high-speed punch in different stages are presented. The approach is applied to improve the reliability growth of high-speed punch in whole life cycle, and in the meantime to provide some useful information for the improvement of the design, manufacturing and using of high-speed punch.

Debugging‐workflow‐aware software reliability growth analysis

SummarySoftware reliability growth models support the prediction/assessment of product quality, release time, and testing/debugging cost. Several software reliability growth model extensions take into account the bug correction process. However, their estimates may be significantly inaccurate when debugging fails to fully fit modelling assumptions. This paper proposes debugging‐workflow‐aware software reliability growth method (DWA‐SRGM), a method for reliability growth analysis leveraging the debugging data usually managed by companies in bug tracking systems. On the basis of a characterization of the debugging workflow within the software project under consideration (in terms of bug features and treatment phases), DWA‐SRGM pinpoints the factors impacting the estimates and to spot bottlenecks, thus supporting process improvement decisions. Two industrial case studies are presented, a customer relationship management system and an enterprise resource planning system, whose defects span a period of about 17 and 13 months, respectively. DWA‐SRGM revealed effective to obtain more realistic estimates and to capitalize on the awareness of critical factors for improving debugging.

Reliability growth analysis of k-out-of-N systems using matrix-based system reliability method

The advent of ever more complex systems in extensive areas of industries hampers efficient and accurate analysis of reliability and effective reliability-based decision making. Such difficulties may arise from the intricate formulation of system failure events, statistical dependence between component failure events, and the convoluted quantification of underlying probabilities of basic events. So-called k-out-of-N systems, which survive or succeed when at least k components are available among the total of N components, give rise to a high level of complexity. This type of systems are commonly introduced to secure a proper level of redundancy in operating engineering systems, but the intricate definition of the system events may elude the system reliability analysis. It is noted that such k-out-of-N systems are often tested and corrected over a certain period of time before their official usage or release in order to assure the target reliability of the system. For the purpose of reliability prognosis based on the data collected from the test period, reliability growth models (RGMs) have been widely used in software and hardware engineering. However, RGMs have been applied mostly to individual components, not at the system level. Furthermore, in complex systems such as k-out-of-N system, it is challenging to relate the reliability growth of components with that of the system. To address this need, in this paper, the matrix-based system reliability (MSR) method is extended to k-out-of-N systems by modifying the formulations of event and probability vectors. The proposed methods can incorporate statistical dependence between component failures for both homogeneous and non-homogeneous k-out-of-N systems, and can compute measures related to parameter sensitivity and relative importance of components. The reliability growths of components represented by RGMs are incorporated into the proposed system reliability method, so that the trend of system reliability growth can be effortlessly evaluated and predicted. Two numerical examples are introduced in this paper to demonstrate the proposed method and its applications: (1) hypothetical systems each consisting of series, parallel and k-out-of-N subsystems, and (2) a simplified high speed train system modeled by multiple k-out-of-N subsystems. Two types of RGMs, i.e. non-homogeneous Poisson process (NHPP) and Duane models are employed in these examples.

Software reliability growth modeling and analysis with dual fault detection and correction processes

ABSTRACTComputer software is widely applied in safety-critical systems. The ever-increasing complexity of software systems makes it extremely difficult to ensure software reliability, and this problem has drawn considerable attention from both industry and academia. Most software reliability models are built on a common assumption that the detected faults are immediately corrected; thus, the fault detection and correction processes can be regarded as the same process. In this article, a comprehensive study is conducted to analyze the time dependencies between the fault detection and correction processes. The model parameters are estimated using the Maximum Likelihood Estimation (MLE) method, which is based on an explicit likelihood function combining both the fault detection and correction processes. Numerical case studies are conducted under the proposed modeling framework. The obtained results demonstrate that the proposed MLE method can be applied to more general situations and provide more accurate results. Furthermore, the predictive capability of the MLE method is compared with that of the Least Squares Estimation (LSE) method. The prediction results indicate that the proposed MLE method performs better than the LSE method when the data are not large in size or are collected in the early phase of software testing.

차세대 고속열차의 신뢰성 성장 분석

In this paper, a reliability growth analysis for a next-generation high-speed train was conducted. First, the high-speed train was decomposed into 6 sub-systems and main equipment of the high-speed train was derived from functional diagrams. Next, failure rates were calculated for each sub-system from the failure data obtained during commissioning tests. Then, reliability growth analysis was conducted for the high-speed train using the Duane model. The results show that activities to increase reliability were carried out throughout the test runs from the reliability growth results.

Fault Diagnosis and Reliability Growth for Information System

This paper aiming at the zero-failure data and uncertain-decision problems exist in the information system reliability growth process, it proposes to build the Bayesian network topology of FMEA. It adopts Leaky Noisy-OR model, and it analyses the probability that the subsystem functional module will go wrong in quantity. It solves the problem of identifying the vague and incomplete information exists in the complex system rapidly and accurately, laying the foundation for further study of the reliability growth comprehensive ability assessment of system based on the Bayesian network. In this paper, on the background of Manufacturing Execution Systems (MES) engineering, aimed at research on models and evaluation methods of reliability growth for MES, enclosing reliability of MES task and design target, reliability growth test and analysis methods, it proposes the goal of MES reliability growth planning.

지지벡터회귀분석을 이용한 무기체계 신뢰도 예측기법

Reliability analysis and prediction of next failure time is critical to sustain weapon systems, concerning scheduled maintenance, spare parts replacement and maintenance interventions, etc. Since 1981, many methodology derived from various probabilistic and statistical theories has been suggested to do that activity. Nowadays, many A.I. tools have been used to support these predictions. Support Vector Regression(SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVM and SVR with combining time series to predict the next failure time based on historical failure data. A numerical case using failure data from the military equipment is presented to demonstrate the performance of the proposed approach. Finally, the proposed approach is proved meaningful to predict next failure point and to estimate instantaneous failure rate and MTBF.Keywords : Support Vector Regression(지지벡터회귀분석), Support Vector Machine(지지벡터머신), Reliability Growth Analysis( 신뢰도 성장분석), Reliability Prediction(신뢰도 예측), NHPP(비동질포아송과정)

Reliability Prediction During Development Phase of a System

Reliability Growth analysis is a process of collecting, modeling, analyzing and interpreting data from the reliability development test program to achieve reliability goal. Many mathematical models have been developed for reliability growth in the literature. In general, it is not possible to fix a priori the model to be used in a given situation. A physical motivation of the problem, based on failure or defect causes/mechanisms, can help in such a choice. The Power Law Process (PLP) is vastly used for monitoring reliability growth during the development test phase. However, in the reliability growth context, the intensity function of the PLP model has two unrealistic situations, viz., it tends to infinity as t tends to zero and tends to zero as t tends to infinity. In this paper, we have proposed Modified PLP model to overcome the second drawback and Doubly Bounded PLP model to overcome both the drawbacks. For the proposed models, the estimates of the model parameters and other quantities of interest have been obtained using Maximum Likelihood approach, for both failure truncated and time truncated data. Reliability Prediction is done using system reliability measures of intensity function and mean time between failures (MTBF). Further confidence intervals of the relevant parameters have been obtained. The procedures developed have been illustrated using numerical examples.

Reliability of EUCLIDIAN: An autonomous robotic system for image‐guided prostate brachytherapy

Recently, several robotic systems have been developed to perform accurate and consistent image-guided brachytherapy. Before introducing a new device into clinical operations, it is important to assess the reliability and mean time before failure (MTBF) of the system. In this article, the authors present the preclinical evaluation and analysis of the reliability and MTBF of an autonomous robotic system, which is developed for prostate seed implantation. The authors have considered three steps that are important in reliability growth analysis. These steps are: Identification and isolation of failures, classification of failures, and trend analysis. For any one-of-a-kind product, the reliability enhancement is accomplished through test-fix-test. The authors have used failure mode and effect analysis for collection and analysis of reliability data by identifying and categorizing the failure modes. Failures were classified according to severity. Failures that occurred during the operation of this robotic system were considered as nonhomogenous Poisson process. The failure occurrence trend was analyzed using Laplace test. For analyzing and predicting reliability growth, commonly used and widely accepted models, Duane's model and the Army Material Systems Analysis Activity, i.e., Crow's model, were applied. The MTBF was used as an important measure for assessing the system's reliability. During preclinical testing, 3196 seeds (in 53 test cases) were deposited autonomously by the robot and 14 critical failures were encountered. The majority of the failures occurred during the first few cases. The distribution of failures followed Duane's postulation as well as Crow's postulation of reliability growth. The Laplace test index was -3.82 (<0), indicating a significant trend in failure data, and the failure intervals lengthened gradually. The continuous increase in the failure occurrence interval suggested a trend toward improved reliability. The MTBF was 592 seeds, which implied that several prostate seed implantation cases would be possible without encountering any critical failure. The shape parameter for the MTBF was 0.3859 (<1), suggesting a positive reliability growth of this robotic system. At 95% confidence, the reliability for deposition of 65 seeds was more than 90%. Analyses of failure mode strongly indicated a gradual improvement of reliability of this autonomous robotic system. High MTBF implied that several prostate seed implant cases would be possible without encountering any critical failure.

Incorporating Generalized Modified Weibull TEF in to Software Reliability Growth Model and Analysis of Optimal Release Policy

Software reliability is generally a key factor in software quality. Reliability is an essential ingredient in customer satisfaction. In software development process reliability conveys the information to managers to access the testing effort and time at which software release into the market. Large numbers of papers are published in this context. In this paper we proposed a software reliability growth model with generalized modified weibull testing effort. Performance application of proposed model is demonstrated through real datasets. The experimental results shown that the model gives an excellent performance compared to other models. We also discuss the optimal release time based on reliability requirement and cost criteria.

SU‐DD‐A2‐06: Reliability Growth of a Fully Automated Robotic IGBT System

Purpose: To evaluate the reliability growth in seed delivery by TJU's EUCLIDIAN, a fully automated robotic system, developed for image‐guided brachytherapy (IGBT). Method and Materials: Important steps in reliability growth analysis are: identification and isolation of failures, classification of failures, and trend analysis. For any one‐of‐a‐kind product, like EUCLIDIAN, reliability enhancement is accomplished through test‐fix‐test cycles of the product. Failure Mode, Effect and Criticality Analysis (FMECA) were used for collection and analysis of reliability data by identifying and categorizing the failure modes. Failures were classified according to severity. Failures that occurred in EUCLIDIAN operations were considered as Non‐Homogenous Poisson Process (NHPP), and the trend was analyzed using Laplace test. For analyzing and predicting reliability growth, commonly used and widely accepted models, Duane's model and Crow's model, were applied. Mean Time Before Failure (MTBF) was used as an important measure for assessing reliability. Results: During pre‐clinical testing, 360 seeds (in 10 cases) were deposited automatically and 6 critical failures were encountered. The majority (5 failures) of which occurred during the first three cases. The Laplace test index was −1.134 (&lt;0), indicating significant trend in failure data, and the failure interval values were gradually becoming larger. Since the failure occurrence interval was increasing, the system reliability exhibited an increasing trend. System's failures distribution followed both the Duane's and Crow's postulations of reliability growth. MTBF was 129.4 seeds, which meant that a full brachytherapy case could be performed without any critical failure. The shape parameter for MTBF was 0.4638 (&lt;1), suggesting positive reliability growth of the EUCLIDIAN. At 0.95 confidence, the lower and upper bounds of the MTBF were 42.8 and 612.9, respectively. Conclusion: Analyses of failure mode strongly indicated a gradual improvement in EUCLIDIAN's reliability. For better consistency in MTBF and reliability prediction, more data are being collected.Acknowledgement: supported by NCI‐R01‐CA091763.

A study on reliability centered maintenance planning of a standard electric motor unit subsystem using computational techniques

The design and manufacture of urban transportation applications has been necessarily complicated in order to improve its safety. Urban transportation systems have complex structures that consist of various electric, electronic, and mechanical components, and the maintenance costs generally take up approximately 60% of the total operational costs. Therefore, it is essential to establish a maintenance plan that takes into account both safety and cost. In considering safety and cost limitations, this research introduces an advanced reliability centered maintenance (RCM) planning method using computational techniques, and applies the method to a standard electric motor unit (EMU) subsystem. First, this research devises a maintenance cost function that can reflect the current operating conditions, and maintenance characteristics, of components by generating essential cost factors. Second, a reliability growth analysis (RGA) is performed, using the Army Material Systems Analysis Activity (AMSAA) model, to estimate reliability indexes such as failure rate, and mean time between failures (MTBF), of a standard EMU subsystem, and each individual component Third, two optimization processes are performed to ascertain the optimal maintenance reliability of each component in the standard EMU subsystem. Finally, this research presents the maintenance time of each component based on the optimal maintenance reliability provided by optimization processesand reliability indexes provided by the RGA method.

Bayesian Reliability—Growth Analysis for Statistical of Diverse Population Based on Non-homogeneous Poisson Process

New armament systems are subjected to the method for dealing with multi-stage system reliability-growth statistical problems of diverse population in order to improve reliability before starting mass production. Aiming at the test process which is high expense and small sample-size in the development of complex system,the specific methods are studied on how to process the statistical information of Bayesian reliability growth regarding diverse populations. Firstly,according to the characteristics of reliability growth during product development,the Bayesian method is used to integrate the testing information of multi-stage and the order relations of distribution parameters. And then a Gamma-Beta prior distribution is proposed based on non-homogeneous Poisson process(NHPP) corresponding to the reliability growth process. The posterior distribution of reliability parameters is obtained regarding different stages of product,and the reliability parameters are evaluated based on the posterior distribution. Finally,Bayesian approach proposed in this paper for multi-stage reliability growth test is applied to the test process which is small sample-size in the astronautics filed. The results of a numerical example show that the presented model can make use of the diverse information synthetically,and pave the way for the application of the Bayesian model for multi-stage reliability growth test evaluation with small sample-size. The method is useful for evaluating multi-stage system reliability and making reliability growth plan rationally.