2020 Annual Reliability and Maintainability Symposium (RAMS)最新文献

{"title":"Reliability Predictions Using Model-Based Criticality-Associated Similarity Analysis","authors":"A. Jackson, T. Jackson, Allura B. Jackson, Kristella B. Jackson","doi":"10.1109/RAMS48030.2020.9153610","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153610","url":null,"abstract":"In this paper, we will describe a methodology called Model-Based Criticality-Associated Similarity Analysis (CASA). The CASA methodology was first introduced to the Reliability Engineering community nearly 20 years ago, at the 48th Reliability and Maintainability Symposium (RAMS) in Seattle, WA [Ref. 1]. This methodology systematically develops a reliability prediction by applying the following empirical-based step-wise conjecture: The ratio of predicted to demonstrated reliability for a new product (i.e., a product that has never been placed in-service) is equal to the corresponding ratio for a similar in-service product that has both its predicted and demonstrated reliabilities adjusted to reflect all the failure and sneak modes, mechanisms, and root causes of the new product. The CASA methodology is practical and efficient when there is newly designed electronics equipment that is sufficiently similar to in-service electronics equipment that has a demonstrated reliability. The application of this methodology will result in a reliability prediction that is more precise than those obtained by using traditional reliability prediction methodologies. With that said, the prerequisite for successful application of the CASA methodology is availability of detailed design and operational/field data. This paper describes an example application of the CASA methodology, in the rapid development of a new and more technologically advanced product that is required to have higher operational reliability and lower cost per unit-function than the predecessor in-service product. Fault/Failure-based modeling can yield meaningful comparisons between the relative design reliability of a new product and the operational/field reliability of a similar inservice product. It can also be used to perform complex reliability assessment in less time. Quantifying design differences allows one to determine adjustment factors that can be applied to the field reliability of the in-service product to obtain a precise and repeatable prediction for the “expected” field reliability of the new product. Since no field or test data are available for a new product design, the characteristics data of a similar in-service product must be used to achieve a degree of confidence in the reliability prediction of the new product. This type of reliability prediction is of great value during the development of the new product’s design reliability features because CASA makes use of knowledge about the impact of fault/failure modes, mechanisms, and root causes that occurred in the field, but which may not be considered by the designers prior to product manufacture and delivery.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133990669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exact Confidence Intervals for the Hazard Rate of a Series Reliability System","authors":"P. Plum, H. Lewitschnig, J. Pilz","doi":"10.1109/RAMS48030.2020.9153656","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153656","url":null,"abstract":"An objective Bayesian method for the determination of upper confidence limits for the hazard rate of an exponential serial reliability system is described. The method is applicable when time-to-failure or failure-in-time data of components are available. Gamma distributions serve as posterior distributions for the hazard rate of the individual hazard rates of the components. A posterior distribution for the system hazard rate is derived by the sum of all components’ hazard rate distributions. Results of an extensive simulation study are presented. The outcome provides strong evidence that coverage probability of this model is always at least as big as the nominal level, regardless of the chosen confidence level or system configuration. In this sense, the proposed method provides conservative but reasonable estimates for the upper limit of the hazard rate. We conclude that this method provides a favorable way for interval estimation of the FIT rate for a serial system, when component data are available. In application, the stated confidence level has an objective and comprehensible interpretation by providing statistical validity. This is of practical importance for the plausibility of a sensitive reliability measure like the FIT rate.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125759862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microgrid Maintenance Strategies for Optimal Reliability and Cost","authors":"Annette Skowronska, Z. Mourelatos","doi":"10.1109/RAMS48030.2020.9153614","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153614","url":null,"abstract":"A physics-based microgrid model has been developed in Simulink with various sources and loads driven by stochastic inputs. Central controls are used to drive down costs while maintaining a reasonable level of reliability. The microgrid is treated as a repairable system where power sources are repaired after failure. We consider the effectiveness of the repair. Repairable systems theory allows us to model repair assumptions such as “good as new” or “as good as old” for example using a renewal process approach. At some point during the operation of the microgrid, some of its subsystems reach their useful life and need to be replaced. This study looks at the trade-off between microgrid reliability and the cost of repair, replacement and operation. The proposed approach can be used to define optimal maintenance strategies. Using repair theory, different repair/maintenance approaches are compared and shown to provide different operational costs and system reliability. An approach is provided to determine an optimal maintenance/repair strategy.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122255004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environmental Stress Screening Approach Based on MIL-HDBK-344A Military Standard","authors":"H. Acar, N. Yilmaz, I. Kilic","doi":"10.1109/RAMS48030.2020.9153612","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153612","url":null,"abstract":"This paper proposes to use MIL-HDBK-344A for Environmental Stress Screening instead of MIL-HDBK-2164. When determining ESS parameters which are related to random vibration (duration in minutes and level in GRMS) and to temperature cycling (number of cycles and temperature rate of change in $^{circ}mathrm{C} /min$.) for various complex systems/equipments based on MIL-HBDK-344A military standard, the outcome reveals as many latent defects as possible in the factory, and consumes less product useful life. This methodology is described by steps with some theoritical assumptions. The study is intended to be used as a guide to make MIL-HDBK-344A easier to implement in practice for electronic systems / equipments.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125061114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal RUL Estimation: A State-of-Art Digital Twin Application","authors":"M. Anis, S. Taghipour, Chi-Guhn Lee","doi":"10.1109/RAMS48030.2020.9153669","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153669","url":null,"abstract":"A real world Industrial IoT set up has paved way for simultaneous monitoring of several sensors at their unique sampling rates. This has realized the need for artificial intelligence tools for robust data processing. However, the large size of input data requires real time monitoring and synchronization for online analysis. As the star concept behind the Industry 4.0 wave, a digital twin is a virtual, multi-scale and probabilistic simulation to mirror the performance of its physical counterpart and serve the product lifecycle in a virtual space. Evidently, a digital twin can proactively identify potential issues with its corresponding real twin. Thus, it is best suited for enabling a physics-based and data-driven model fusion to estimate the remaining useful life (RUL) of the components. Traditional RUL prediction approaches have assumed either an exponential or linear degradation trend with a fixed curve shape to build a Health Index (HI) model. Such an assumption may not be useful for multi-sensor systems or cases where sensor data is available intermittently. A common constraint in the industry is irregular sensor data collection. The resulting asynchronous time series of the sporadic data needs to be an accurate representation of the component’s HI when constructing a degradation model. In this paper, we extend the Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) technique to generate RUL prediction within a digital twin framework as a means of synchronization with changing operational states. More specifically, we first use LSTM encoder-decoder (LSTM-ED) to train a multilayered neural network and reconstruct the sensor data time series corresponding to a healthy state. The resulting reconstruction error can be used to capture patterns in input data time series and estimate HI of training and testing sets. Using a time lag to record similarity between the HI curves, a weighted average of the final RUL estimation is obtained. The described empirical approach is evaluated on publicly available engine degradation dataset with run-to-failure information. Results indicate a high RUL estimation accuracy with greater error reduction rate. This demonstrates wide applicability of the discussed methodology to various industries where event data is scarce for the application of only data-driven techniques.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130062222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability Analysis of Tree-Structured Systems Using Characteristic Function","authors":"Rui Sun, Weidong Wang, Li Chen, Wenyi Zhang","doi":"10.1109/RAMS48030.2020.9153627","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153627","url":null,"abstract":"This paper provides an efficient algorithm for computing the reliability of both tree-structured and hierarchical array systems with k-out-of-n failure criteria. The algorithm utilizes characteristic functions and avoids working directly with probability mass functions. A case study is investigated to illustrate the efficiency of the proposed algorithm. Compared with conventional analysis methods, the proposed algorithm shows superiorities in both efficiency and feasibility. It may significantly help system designers facilitate the reliability analysis and optimization of large-scale tree-structured and hierarchical array systems, especially in model-based systems engineering (MB SE).","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130077616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Model-Based Systems Engineering on Reliability Growth","authors":"B. Haughey","doi":"10.1109/RAMS48030.2020.9153637","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153637","url":null,"abstract":"Reliability growth is the improvement in the reliability of a product based on changes in the product’s design and the manufacturing processes of the design. Product and process changes are the result of taking an action to resolve an identified issue. Image 1 shows how issues are identified two ways; “seen” failure modes as a result of testing and product usage and “unseen” failure modes as a result of engineering calculations and technical discussion. All design changes will have issues (“seen” and “unseen”) in the product design, manufacturing and assembly, and service. Reliability growth has been dependent on subjecting a product to physical testing to identify product deficiencies over the product development lifecycle. R&M in a Model-based System Engineering Environment will significantly reduce the amount of physical testing and should increase the number of discovered (seen) failure modes much sooner in the design cycle. However, that will not eliminate the need to conduct technical risk analysis to identify the unseen failure modes. Product launches are getting shorter and customers are requiring higher quality and reliability. That is why the latest SAE J1739 FMEA Standard recommends conducting a preliminary risk assessment to prioritize which areas of the product design require DFMEA and which areas of the manufacturing and assembly process require PFMEA. Conducting FMEA on the critical areas of the design and process will support the identification of unseen failure modes. However, there is a methodology to enhance the ability to identify unseen failure modes as a result of conducting a focused review of tested products. The methodology is called Design Review Based on Test Results (DRBTR). The mindset of DRBTR is to look for buds of problems (“unseen” failure modes) by studying tested products in a formal review. DRBTR takes advantage of running limited test samples by documenting observations that will address concerns for both the product and process.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126336941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strategic Uses of Different Shapes and Shifts in Weibull Plots","authors":"S. Jayatilleka","doi":"10.1109/RAMS48030.2020.9153581","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153581","url":null,"abstract":"During the product development processes, components and subsystems go through test-fix-test cycles until the final configurations surpass the design life to exceed the reliability goals. When failure modes are uncovered before reaching the design life, design configurations are improved to eliminate failures. Early failures can be mixed with several failure modes commonly lead to lower shape parameters. Systems that are mature that survive close to design life may have higher and steeper slope parameters. During the component-subsystem development process and design maturity, shape parameter changes associated with reliability growth are illustrated with examples. As designs reach or surpass design life with design maturity, failure modes can be single out to unique shape parameters. Separating different shapes and patterns of Weibull curves help identifying different failure modes thereby leading to failure mode-wise reliability analysis. Such analysis help prioritizing the fixing process of each failure mode based on returns on investment.On the other hand, if a product was launched before it reaches the design life and meets reliability goals, field failures become inevitable. Such field failures can also be modeled with Weibull Analysis. Parts that fail short of the design life are commonly called under-developed designs. Such designs can be mixed with multiple failure modes. Different shapes and shift of curves can be strategically isolated for separating design weaknesses and investigations before catastrophic failures erase evidence of root causes. For example, shape parameters and patterns of underdeveloped parts can be helpful in isolating manufacturing issues from design issues. This paper addresses how to perform data analysis in order to strategically enhance the further use of Weibull plots, its shapes and pattern recognition for speedier product development and customer satisfaction.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127724082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The New NASA Approach to Reliability and Maintainability","authors":"Harry W. Jones","doi":"10.1109/RAMS48030.2020.9153701","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153701","url":null,"abstract":"The new NASA technical standard on R&M has moved away from requiring specific R&M activities during each of the traditional project phases to instead developing and planning the implementation of the R&M requirements to meet the top-level project R&M objectives. The emphasis is on providing the evidence to show that the R&M requirements are met, rather than on conducting specific prescribed R&M activities. The technical standard on R&M defines a comprehensive hierarchy of specific R&M objectives and identifies particular strategies to implement them at each level. That is, the top level R&M objective is defined and then one or more design strategies to implement it are developed immediately before the next lower objectives are defined and the strategies to achieve those are designed. The objectives are the R&M requirements, and the strategies are the hardware designs or operations plans developed to meet these requirements. The new R&M process is aligned with the systems design process and helps ensure that the methods to meet the R&M requirements are built into the design.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128006589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate Reliability Prediction Based on RIDM","authors":"Qunyong Wang, Dongmei Chen, Hua Bai, Fajian Shi","doi":"10.1109/RAMS48030.2020.9153596","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153596","url":null,"abstract":"The reliability of an item is the probability that it will adequately perform its specified purpose for a specified period of time under specified environmental conditions [1]. Uncertainty [2] in reliability prediction for complex electronic systems/equipment is mainly originated from the incomplete considerations on the specified environmental conditions and on the impacts to the equipment. This paper focuses on the incomplete considerations on the space radiation environment (SR see Acronyms in table 1) and their impacts on complex electronic equipment in satellites, airplanes, and ground network computers working daily in SR. Based on Risk Informed Decision Making (RIDM) analysis framework [3], this paper proposes an accurate reliability prediction method to accommodate SR and SR impacts on complex electronic equipment to avoid uncertainties originated from the incomplete considerations on SR theoretically more complete or accurate than the traditional methods, such as FIDES [4], MIL-HDBK-217 [5], etc, which has not considered SR. This paper is creative on the following 3 novel points. (1) SR reliability concept: For accurate reliability prediction, this paper proposes an SR reliability concept based on RIDM framework. The concept accommodates SR and SR impacts critical to mission success by the SR reliability definition and the RIDM 5 steps analysis method to have a close loop systematic thinking on incomplete considerations on SR. (2) SR reliability model: For an accurate reliability prediction based on the SR reliability concept, this paper proposes a new SR reliability model focused on the SR impact rate for complex electronic equipment, connecting the incomplete considerations of SR with mission success in a close loop by 1) The relationship [6] between Reliability and SR impact rate, 2) The relationship [6] between SR impact rate and TID, DD, and SEE impact rate, 3) The relationship between SEE impact rate and SEE effect rate. (3) SR reliability prediction application and verification: This paper provides 3 case studies on satellite, avionics, and ground network computer applications. Additionally, the SR impact rate for avionics of a satellite navigation receiver prototype is predicted and compared with the test results [7] with good agreement. Case studies show that for safety critical applications, the SR impact rate is significant and not negligible. And the SR reliability concept and model is practical and useful for displaying possible key contributions to SR mitigation strategies or Defense-in-depth systematically for mission success.This paper gives an example for the accurate reliability prediction based on RIDM, focusing only on accommodating SR. However, as a general systematic method, it can be applied in broader areas on more SR and non-SR stresses.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123363283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}