This paper describes the organization that has been evolved for the collection and analysis of field failure data. A summary breakdown is given of the data collection organization, the Data Format and the methodology of Data Analysis.
Preliminary studies show the system is working.
The concept of superpopulation has been established in order to judge the relative importance of data as it is accumulated.
Over the past several years, numerous industries have increased their adoption of the systems modeling language (SysML®) and model-based systems engineering (MBSE) as a core practice within their engineering lifecycles. However, the introduction of SysML and MBSE methodologies has not yet yielded many of the originally envisioned benefits. System models are becoming larger and more complex and many large MBSE projects continue to experience problems with model integration, repository performance, and model lifecycle management. The root cause is the failure to recognize the MBSE digital environment as a complex engineering information processing system that requires the same rigor and development processes as the system-ofinterest (SoI) it is designing. This article describes how three future of systems engineering (FuSE) agility foundation concepts (system of innovation, effective stakeholder engagement, and continuous integration) directly address some of the problems seen in adoption, deployment, and sustainment of the MBSE digital environment as an SoI.
“Is the automation making us safer?” This question is coming up with increasing frequency as the industry looks to increasingly levels of automation to solve a host of different technical and business challenges that we are facing. Previous papers have looked into such questions as the appropriate level of automation, mode confusion, and envelope protection approaches. This paper looks at a more basic problem: Are we able to precisely articulate what it is we want the automation to do and rigorously trace these requirements down to their granular point of implementation?
The author is currently working as the consultant to a shipyard that is producing a high-specification jackup rig for operation in the North Sea. This rig contains ten major software systems made by several different suppliers and their subcontractors and divisions. Making sure these major rig systems work properly together in critical situations has traditionally been quite difficult. Detailed requirements are scattered throughout thousands of pages of dozens of different specifications. Accurate manual tracking of these requirements is extremely difficult. This paper reports on the application of Model-Based Systems Engineering techniques common in the aerospace and automotive industries to tighten up the accuracy, traceability, and accountability for the rig-level safety automation design.
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