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

{"title":"Trade-off Between Sample Size and Test Time: Lessons Learned in Optimizing Reliability Demonstration Test Plan","authors":"A. Balasubramanian, P. Shrivastava","doi":"10.1109/RAMS48030.2020.9153592","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153592","url":null,"abstract":"Intelligent completion systems are permanently installed downhole in severe operating conditions and are designed to achieve high operational reliability in high temperature and pressure applications. An accelerated reliability demonstration test (ARDT) is typically required to demonstrate if the system has met its reliability target. ARDTs are typically setup as a success test and all test specimens are expected to survive the test time. To satisfy the constraints on testing expense and product development time, a key step in designing an ARDT plan is to perform a trade-off between sample size and test time to meet the reliability target at the desired confidence level. Generally, a large sample size results in a shorter test time and vice versa.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"83 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":"126401281","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":"Text Mining-based Research on Aircraft Faults Classification and Retrieval Model","authors":"Xingxing Xu, Shenghan Zhou, Yiyong Xiao, Wenbing Chang, Fajie Wei, Ming Yang","doi":"10.1109/RAMS48030.2020.9153588","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153588","url":null,"abstract":"With the development of information technology, a large number of equipment management systems have been used in the aviation field. These systems generally have failure management functions. After the failure occurs, it is necessary to input the text information of equipment failure in time, such as “combination switch damage”, “intake pipe rupture” and other failure phenomena. In addition, the corresponding failure causes and troubleshooting methods also need to be fully recorded. Therefore, as the system has been used, a large number of equipment failure texts will be generated. If we can make full use of these unstructured data and discover the knowledge through text mining, it will be of great significance to fault analysis and maintenance decision-making.","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":"122597526","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 Management of RAM Data Using OT and IT Infrastructure in Oil & Gas Operations","authors":"M. R. Mahmood, Brandon T. S. Tan","doi":"10.1109/RAMS48030.2020.9153689","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153689","url":null,"abstract":"SUMMARY & CONCLUSIONSManaging safety-critical systems in real-time, across geographically diverse multiple oil & gas installations, both offshore and onshore, is in itself a huge challenge for any corporation. PETRONAS, through its Exploration, Development & Production subsidiary PETRONAS Carigali Sdn Bhd, has successfully deployed a digital solution to remotely manage multiple safety-critical systems under different operational technology (OT) sites from a centralized location in real-time over its existing corporate information technology (IT) infrastructure. Thus, creating the first approach to reliability domain-based information data lake to assist real time operation of safety-critical systems and improve production efficiencies. In this paper, readers be given an insight into the journey of PRiME-IO, the digital solution currently overlooking real-time operation of multiple OT safety-critical systems such as DCS and safety PLCs across hundreds of offshore and onshore sites. Challenges in OT/IT convergence, including but not limited to cybersecurity and data transmission reliability, and the solutions adopted to overcome these, will be discussed at length. Apart from the hardware and infrastructure aside, key human factor considerations such as competency upskilling of engineers and technicians, enabling fast feedbacks on decisions, as well as changes in work processes and improved productivity, will also be highlighted. Tangible operational benefits such as lower risk & exposure to frontline operations, faster response time and lower day-to-day logistic costs will also be demonstrated","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":"131285280","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":"Engaging Supportability Analysis through Model-Based Design","authors":"R. Beshears, Andrew Bouma","doi":"10.1109/RAMS48030.2020.9153646","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153646","url":null,"abstract":"BackgroundModel-based designs (MBD) provide an architecture framework to leverage design information within a digital thread for conducting supportability analyses. These analyses require common source data to develop assessments, trade studies, and provide design influence recommendations for the product. Design artifacts such as requirements, block diagrams, drawings and schematics provide the information needed to develop supportability analyses (e.g., reliability analyses, Failure Modes, Effects, and Criticality Analysis (FMECA), fault tree analyses, testability analyses, level of repair analyses, and life cycle cost analyses). Model-based design capabilities support a common thread of data for utilization across multiple discipline areas and showcase the ability to employ a common model to perform various analyses. Functional architectures and diagrams contextualize a design, providing information needed by several engineering functions. Design engineering utilizes these functional representations to set up the detailed design. Supportability engineering design considerations are critical to ensuring that the design supports reliability, maintainability, testability, safety, and logistics features necessary to optimize product support. These principal supportability areas all require engagement and analyses of the design functions to achieve supportability goals. A model-based design paradigm provides a capability where the functional design aspects are integrated and streamlined rather than segregated. Benefits of the model-based design include analyses that are more efficient, enhanced design influence capability, and integrated design packages. Additionally, model-based designs foster more integration of supportability analyses within the hardware and software design. Design trade-offs and studies within the model provide opportunities for broader supportability assessment capability in comparison to stand-alone analyses. However, challenges such as configuration control of model updates within a multi-user environment must be comprehended and addressed to prevent unintended changes in effort. This paper provides an approach for how model-based designs can integrate principal supportability analyses and capabilities during the design phase of a program along with the challenges and issues faced within this environment.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"26 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":"131925752","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":"Origin of l/f Noise-Active Degradation Generating Entropy","authors":"A. Feinberg","doi":"10.1109/RAMS48030.2020.9153628","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153628","url":null,"abstract":"Noise measurements analysis has been associated with degradation. In particular one type is called 1/f noise and is not fully understood. In the time domain, the signal has a random noise appearance. However, in the frequency domain, the spectrum goes as 1/f in intensity at low frequencies. Noise issues, of course occur at all frequencies. In reviewing the literature, we note that 1/f noise in particular seems to be strongly related to aspects in materials that can be interpreted in terms of degradation in materials (i.e. disorder). In this paper we describe some key aspects of 1/f noise found in the literature and discuss how observations relate to generated entropy. We conclude from the literature the 1/f noise region is of paramount importance to observing subtle degradation occurring in materials. In fact, we find that active degradation is the root cause of 1/f noise in materials. We then use a thermodynamic frame work to help interpret our view. We model the 1/f spectral region using an entropy model. We suggest two models. Results help to provide a broader understanding of 1/f noise, identify the region of the spectrum related to the onset of degradation, and show how it can be used to do prognostics. Experiments are suggested to demonstrate how 1/f noise measurements can be used as a prognostic tool for reliability testing to identify and predict degradation over time.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"21 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":"130521549","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":"Agile FRACAS in Production Manufacturing","authors":"Jason D. Tanner","doi":"10.1109/rams48030.2020.9153660","DOIUrl":"https://doi.org/10.1109/rams48030.2020.9153660","url":null,"abstract":"SummaryThis paper provides a detailed and methodical approach towards the implementation of an agile Failure Reporting, Analysis and Corrective Action System (FRACAS). It is important that the user already have tools in place for data collection, and for the management of FRACAS activities so that this methodological proposition can be accomplished. The method is a data-driven approach to collecting failure information and utilizing / allocating resources with maximum efficiency. Current FRACAS methods are fairly basic and not set up to allow the data to lead a program towards task and resource prioritization. Per MIL-STD-2155 [1], a Failure Review Board (FRB) is the primary mechanism for the review of failure trends, corrective action status, and to assure adequate corrective actions are taken. Additionally, it describes failure reporting simplistically as it pertains to individual failed items, not necessarily to failure trends and process issues. There is nothing specific in the document to drive high-rate production manufacturing environments towards utilizing failure trend information to identify high value / volume failures, thus developing the needs for an investigation into Root Cause and Corrective Action (RCCA). This proposal works under the following assumptions/steps:1.There exists a method to capture failure information and data2.The program can properly delineate production manufacturing issues from other non-production manufacturing issues, such as Development & Verification Testing, Qualification Testing, Mission Testing, etc.3.Leadership is not just in agreement with the process, but will act as an advocate4.Appropriate risk analysis and fault tree analysis techniques are utilized and presented5.An appropriate command media process already exists for the documentation of implementation requirements","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"179 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":"121090152","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":"Lessons from Deploying Predictive Analytics on Manufacturing Shop Floor","authors":"Alex Romriell, R. Niculescu, David Kessler, Tracey Kroll","doi":"10.1109/RAMS48030.2020.9153728","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153728","url":null,"abstract":"Machine Learning techniques have been successfully deployed to provide advanced warning that certain errors are going to occur on a set of automated fiber placement (AFP) machines manufacturing high tech plane parts. This allows proactive actions to be taken to prevent unnecessary downtime and increase machine throughput.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"14 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":"127604218","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 RAMS Journey for MicroTurbine Power Generation","authors":"Evan Franke, Gérard Cohen","doi":"10.1109/RAMS48030.2020.9153587","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153587","url":null,"abstract":"When considering Reliability, Availability, Maintainability and Safety (RAMS) for a technically advanced device like Capstone’s microturbine power generator, we needed to explore many facets of its function, application, field location and operational performance. These variables became even more challenging when product installations took on a global scale and included various challenging and often isolated locations (oil platforms, landfills, remote islands, and more). Our RAMS journey for microturbines contains helpful guidance for any companies seeking a roadmap of how to incorporate reliability and availability analytics and principles into the fabric of their organization, especially through the early phases of company maturity.For early product development and initial launch, we used mean time between failures (MTBF) and Failure Rate (Lambda) as straightforward metrics for goal-setting and prioritizing improvement programs. A FRACAS database was used for collecting post-shipment data, and was essential for providing raw data for our analytics. We also began identifying technical staff that were talented in customer communication, who quickly became critical customer satisfaction champions during problem identification and resolution activities.As our product design stabilized, we began measuring customer service aspects such as response time and repair times, which we combined with reliability metrics to provide estimates of Availability. In order to capture the best-rounded understanding of the product RAMS performance, we looked at Availability metrics across different regions and markets, with each illuminating different challenges. Using Availability also provided a single metric that aligned our organization toward customer satisfaction by identifying how all employees could play a direct role in reliability and service improvement, regardless of their functional department.As our product fleet and customer base continues to grow and globalize, we are now engaging with our distribution business parmers to ensure that they adopt our culture and principles of customer satisfaction, as they are increasingly the face of our product to our end users. To this end, we use tools such as Key Performance Indicators (KPIs) and “model behaviors” to align and clarify roles and responsibilities between our companies, and to hold our business partners accountable for their role in customer satisfaction.","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":"130698788","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":"Why Stochastic Models That Are So Famous, Become Infamous In Reliability Engineering","authors":"M. Kaur","doi":"10.1109/RAMS48030.2020.9153643","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153643","url":null,"abstract":"Stochastic models have interesting applications in predicting random behavior for varied problems of engineering and sciences. These models are defined as a family or collection of a set of random variables defined on a time dependent sample space, a sample space also known as state space. In reliability engineering, evaluating the performance of a system for a specified time using stochastic models started in the twentieth century. Further different forms of stochastic models like Markov chain, renewable models, regenerative models were used in performance evaluation for system improvements. Studies on these models have shown tremendous capabilities of evaluating performance of a simple system to complex systems. However, it is failing to attract the majority of current practitioners as well as academic researchers for bringing more application oriented or improved work based on these models from the last few decades/years (ref. number of publications in top reliability journal viz RSS, IEEER, Microelectronics Reliability). This paper seeks to understand why these models are so famous in reliability engineering in the early years of the reliability discipline and, becoming infamous today as per collected statistics of academic literature, as well as diverting the mindset of the scientific academic community towards other approaches. It also provides a comparative discussion on the model research that has been carried out so far and discusses future insights on how it can serve as a better model to estimate reliability using a hybrid technique for big industrial systems process.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"573 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":"132393207","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":"Early Combined Safety - Security Defense in Depth Assessment of Complex Systems","authors":"N. Papakonstantinou, Joonas Linnosmaa, Ahmed Z. Bashir, T. Malm, Douglas L. Van Bossuyt","doi":"10.1109/RAMS48030.2020.9153599","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153599","url":null,"abstract":"Safety and security of complex critical infrastructures is very important for economic, environmental and social reasons. The interdisciplinary and inter-system dependencies within these infrastructures introduce difficulties in the safety and security design. Late discovery of safety and security design weaknesses can lead to increased costs, additional system complexity, ineffective mitigation measures and delays to the deployment of the systems.Traditionally, safety and security assessments are handled using different methods and tools, although some concepts are very similar, by specialized experts in different disciplines and are performed at different system design life-cycle phases.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"34 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":"132252160","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}