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

{"title":"A Model-Based Framework for Analyzing the Security of System Architectures","authors":"Kit Siu, H. Herencia-Zapana, D. Prince, A. Moitra","doi":"10.1109/RAMS48030.2020.9153607","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153607","url":null,"abstract":"We introduce a compositional, model-based framework for modeling, visualizing and analyzing the security of system architectures. This work extends the framework we developed previously for analyzing safety [1]. With this extension, our framework can be used to analyze both safety and security, however this paper focuses on security. The major contribution of this paper is setting the terminology and methodology for building a tree for analyzing the security of a system. Defining precisely the qualitative and quantitative aspects of the tree is very important-just as fault trees are rooted in the theory of probability, we want our tree to be built on solid mathematical foundation. Based on [2] and [3], attack-defense tree is a better representation of a system over attack trees because the latter only captures attack scenarios and does not model the interaction between attacks and the defenses that could be put in place to guard against the attacks. More importantly, security of a system is constantly evolving–as better control measures are put in place, more sophisticated attacks are implemented. Therefore, modeling only attacks without considering the defenses in place is very limiting. Guided by some of the formalisms introduced in [2] [3], we extended their concepts to include guidelines and considerations from DO-326A and DO-356A so that the terminology used in the tree is relevant to the aviation industry. We reference measure theory and order theory to define functions for the quantitative aspects of the tree. We also made sure that the measures were consistent with the intuition of a security design engineer. Finally, we give an example of the modeling language and the attack-defense tree that is automatically generated.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"1 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":"125436993","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":"Behavior Modeling for Cybersecurity","authors":"L. Meshkat, Robert L. Miller, Christine Hillsgrove, James L. King","doi":"10.1109/RAMS48030.2020.9153685","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153685","url":null,"abstract":"A significant percentage of cyber security incidents can be prevented by changing human behaviors. The humans in the loop include the system administrators, software developers, end users and the personnel responsible for securing the system. Each of these group of people work in a given context and are affected by both soft factors such as management influences and workload and more tangible factors in the real world such as errors in procedures and scanning devices, faulty code or the usability of the systems they work with.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"45 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":"127452150","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":"A Novel Framework to Assist the Novice in Defining Failure Modes for System-level Software FMEA","authors":"Naoko Okubo, Kohsuke Namihira, Hiroki Umeda, Yasushi Ueda, M. Katahira, Shuji Morisaki","doi":"10.1109/RAMS48030.2020.9153603","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153603","url":null,"abstract":"System-level Software Failure Mode and Effects Analysis, called as FMEA, that is highly focused on software behavior (here in after SS-FMEA) is difficult to identify effective failure modes which may lead to a serious accident due to the systemic nature of failures rather than random hardware failures. The study has modeled insights derived from the best practices of space system’s SS-FMEA and proposed a framework to assist the novice engineer in identifying high impact failure modes.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"68 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":"127477607","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":"Real-Time Optimization of Maintenance and Production Scheduling for an Industry 4.0-Based Manufacturing System","authors":"Mageed Ghaleb, S. Taghipour, Hossein Zolfagharinia","doi":"10.1109/RAMS48030.2020.9153721","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153721","url":null,"abstract":"The adaption of state-of-the-art inventions in information technology and industrial informatics in manufacturing has led to the advent of Industry 4.0, commonly known as the fourth industrial revolution. Industry 4.0 will take manufacturing productivity and quality to new levels and create enormous opportunities for business and revenue growth. Unlike classical manufacturing systems, Industry 4.0-based manufacturing systems are supported by several advanced technologies (known as Industry 4.0 concepts), which include cyber-physical systems (CPS) and internet of things (IoT), among other Industry 4.0 concepts. The adoption of such technology (i.e., Industry 4.0) allows for the delivery of real-time actionable data for smart decision-making. In order to fully realize the potential of such technologies, real-time decision making should be present in all aspects of the manufacturing process. This includes two core components of manufacturing: maintenance and production scheduling.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"29 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":"122194200","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 Crude Unit Overhead Piping Based on Wall Thickness Degradation Process","authors":"E. Bediako, Yisha Xiang, Susan Alaswad, Liao Ying, L. Xing","doi":"10.1109/RAMS48030.2020.9153611","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153611","url":null,"abstract":"Assuring the reliability of crude unit pipelines in the downstream oil and gas industry is highly essential since unexpected failures of these pipelines can result in a number of negative impacts to the business, including safety, environmental, and economic impacts. The objective of this work is to understand the degradation behavior of the piping system so we can know in advance when the degraded pipeline will reach the minimum thickness threshold.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"22 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":"116853655","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":"A Fractal-Cluster-Based Analytical Model for Spatial Pattern of Congestion","authors":"Xiangyu Zheng, N. Huang, Yanan Bai, Shuo Zhang","doi":"10.1109/RAMS48030.2020.9153714","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153714","url":null,"abstract":"Research has shown that spatial patterns of congestion is neither compact as expected by typical model of cascade dynamics nor purely random as in percolation theory. Analyzing spatial patterns of congestion is critical for mining spatial-temporal characteristics of congestion evolution. Spatial patterns of congestion are the result of congestion interaction, which appears as the dependency relationship of the adjacent edges and the dependency relationship of the non-adjacent edges with a certain range in the network. Previous models which analyze spatial patterns of congestion mainly considers the dependency relationship of the directly connected edges, but lack the consideration of the dependency relationship of the indirectly connected edges. Therefore, this paper presents a fractal-cluster-based analytical model considering the dependency relationship of the indirectly connected edges to describe the dominant mechanism governing the formation and evolution of spatial pattern of congestion. First, we introduce the edge dependency coefficient to quantitatively describe the dependency strength of the adjacent edges. Next, we regard the basic fractal element of the network as a cluster and introduce the cluster dependency coefficient to quantitatively describe the dependency relationship of the non-adjacent edges with a certain range in the network. Finally, we construct a weighted network in which the weight of edges represents the congestion level of edges and introduce a novel load transfer mechanism to describe the results of congestion interaction. Based on this, a fractal-cluster-based congestion evolution model is established to analyze spatial patterns of congestion. To quantify spatial pattern, we use the fractal dimension of the weighted network dB (a measurement of objects’ irregularity). The simulation comparison results have verified the feasibility of this indicator. Furthermore, simulation results have shown that our proposed model is more in line with the observed congestion propagation process, which verifies the effectiveness of our proposed model. This work can give precious hints on which step of the process is responsible for the congestion duo to the its mechanistic analysis of spatial patterns.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"4 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":"116881759","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":"Intersection of Systems and Reliability Engineering during New Product Development Process","authors":"S. Jayatilleka","doi":"10.1109/RAMS48030.2020.9153653","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153653","url":null,"abstract":"The time spent from the conceptual stage to the final product design, development and deployment needs to be competitively small in order to be successful in today’s market place. Working with fewer samples within fewer numbers of design iterations, reducing the time between two design iterations, and achieving higher reliability among such iterations are some of the main challenges of the new product development (NPD) process. In this process, strategies of both systems and reliability engineering can be utilized for speedier goal achievement at different NDP stages. Examples from the appliance industry are used to demonstrate the utility of these strategies.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"17 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":"117045207","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":"NASA Quality Assurance in an MBSE World","authors":"S. Cornford, David Kotsifakis, S. Beckman, M. Feather, J. Evans","doi":"10.1109/RAMS48030.2020.9153683","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153683","url":null,"abstract":"Over the past decade or so, improvements and changes in descriptive models of systems have been occurring. This has mainly come about in the shift from a document-centered approach to an approach using a dedicated language known as the Systems Modeling Language (SysML). SysML has allowed Systems Engineers (SEs) to consolidate information about a system and even automate some tasks leaving the SEs to concentrate on the actual engineering and decision-making. Many of our quality assurance (QA) tasks either overlap or interface with systems engineering tasks, thus, it is important for quality engineers to understand and contribute to these system engineering models. We will explore some of the ways in which QA processes and products may be model within the framework of SysML. First let us look at how quality can be assured by using requirement elements in a systems model.","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":"132806531","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":"Systems Engineering Modelling Diagrams as Prerequisites to Failure Mode and Effect Analysis","authors":"S. Jayatilleka","doi":"10.1109/RAMS48030.2020.9153649","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153649","url":null,"abstract":"Summary & ConclusionsFailure mode and effect analysis (FMEA) process starts with several key inputs. A few such traditional inputs are the older generation FMEAs, field failure reports, corrective actions and lessons learned. During the past two decades there had been several diagrams used as important FMEA inputs. The most popular diagrams of all diagrams had been the boundary diagram and the parameter diagram that were used to discover hidden functional requirements and failure modes for Design FMEAs. Similarly, the Process Flow Diagram had been used to discover process steps as input to Process FMEAs. This paper discusses several other diagrams depending on the stage of the product development process. FMEAs begin with Functional Requirements. The two main issues affecting the effectiveness of DFMEA are the (i) poorly written functional requirements and (ii) the missing functional requirements. The main connection and the contribution of this paper to DFMEA is the discovery process of functional requirements, otherwise missed. Once the functional requirements are discovered, the rest of the elements of FMEAs are derived from those functional requirements. For example, failure modes are derived as over-function, under-function, or no function, etc. Therefore, missed and poorly written requirements are going to affect the effectiveness of the all elements of FMEA, thereby the product designed level for reliability. The requirements come from different sources. They could be performance, regulatory, safety, or environmental, to mention a few. As mentioned before, if requirements are missed in a FMEA, verification and validation of that requirement is going to be missed. In addition, poorly written requirements lead to inadequate verification and validation test plans. The traditional Boundary and Parameter Diagrams have been influential as a multidimensional tool in discovering the initial requirements. To strengthen the multidimensional requirement discovery process, systems engineering modeling language (SysML) offers several other diagrams. Few examples are the activity diagrams, sequence diagrams, state machines diagrams and use case diagrams. This paper discusses such popular and useful SysML diagrams used across new product development processes to discover functional requirements that may be missed otherwise and feed the DFMEA to have a good start to an effective FMEAs. Examples are provided from automobile, wind turbine, and heating & air-conditioning industries.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"24 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":"128346377","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":"Contribution of Risk from Human Failures in PRA Models","authors":"J. Weglian, J. Riley, M. Presley","doi":"10.1109/RAMS48030.2020.9153712","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153712","url":null,"abstract":"Large industrial facilities, such as commercial nuclear power plants, still require human operators to respond to abnormal conditions. Failures of these operators to perform the appropriate actions can lead to significant consequences. Human failure events (HFEs) are modeled in probabilistic risk assessment (PRA) models for these plants to consider the consequences of these failed actions. These PRA models explicitly consider various types of failures, including failures to align equipment prior to an event, which leaves that equipment unavailable to respond, and failures of human actions after the abnormal event that are designed to mitigate the event.","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":"134405247","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}