REG NASA-LLIS-0825-2000 Lessons Learned System Reliabilty Assessment Using Block Diagraming Methods.pdf
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1、Best Practices Entry: Best Practice Info:a71 Committee Approval Date: 2000-04-19a71 Center Point of Contact: JSCa71 Submitted by: Wil HarkinsSubject: System Reliabilty Assessment Using Block Diagraming Methods Practice: Use reliability predictions derived from block diagram analyses during the desig
2、n phase of the hardware development life cycle to analyze design reliability; perform sensitivity analyses; investigate design trade-offs; verify compliance with system-level requirements; and make design and operations decisions based on reliability analysis outputs, ground rules, and assumptions.P
3、rograms that Certify Usage: This practice has been used on the Orbiter Project and Space Station ProgramCenter to Contact for Information: JSCImplementation Method: This Lesson Learned is based on Reliability Practice number PD-AP-1313, from NASA Technical Memorandum 4322A, Reliability Preferred Pra
4、ctices for Design and Test.Benefit:Reliability block diagram (RBD) analyses enable design and product assurance engineers to (1) quantify the reliability of a system or function, (2) assess the level of failure tolerance achieved, (3) identify intersystem disconnects as well as areas of incomplete d
5、esign definition, and (4) perform trade-off studies to optimize reliability and cost within a program. Commercially available software tools can be used to automate the RBD assessment process, especially for reliability sensitivity Provided by IHSNot for ResaleNo reproduction or networking permitted
6、 without license from IHS-,-,-analyses, thus allowing analyses to be performed more effectively and timely. These assessment methods can also pinpoint areas of concern within a system that might not be obvious otherwise and can aid the design activity in improving overall system performance.Implemen
7、tation Method:Analysis methods described below make use of RBD analyses and commercially available software tools to analyze NASA space system designs. They are equally useful for analyzing mechanical and electrical systems and identifying potential deficiencies in system redundancy and/or reliabili
8、ty performance based on RBD assessments derived from drawings, schematics, and system specifications and documentation.A detailed understanding of system architecture and functionality is necessary to assess system reliability using these types of quantitative analyses. The output of this analysis i
9、s valuable to the design and engineering functions on a program. It is more useful if a concurrent relationship exists between the product assurance team activity performing the analysis and the engineering design team, since design alterations and improvements can be made in near real time. These m
10、ethods combine research, drawing review, reliability analyses, and the use of software automation.When this approach is taken, it is recommended that a team of individuals be involved to bring the necessary skills to the analysis, to share the workload, and to ensure that all technical areas of the
11、analysis are covered.The RBD Technique:The RBD process involves developing block diagrams of a system or of a systems function (tasks for which hardware/software systems were designed). JSC analysis personnel have developed both system and system function models. Experience shows that more benefits
12、are realized from the system function models. When a function is represented as a block diagram, the models should include all operational components of the systems that are involved in the function and reflect component redundancy and subsystem-to-subsystem connectivity. The models are developed wi
13、th a commercially available software tool and, with the proper inputs, are assessed for overall system reliability and design reliability concerns.Software analysis tools are an essential part of the JSC RBD analysis process. For these analyses, JSC personnel use commercially developed software for
14、a personal computer. As with any analysis, it is critical that all involved parties understand (1) what items were used for input and what assumptions were made, (2) what calculations were performed, and (3) what interpretations can be made from the outputs.1. Inputs. To create an RBD, it is necessa
15、ry to collect three types of information about the system being studied: functional systems architecture data, component reliability data, and Provided by IHSNot for ResaleNo reproduction or networking permitted without license from IHS-,-,-mission times. Architecture defines the redundancy interrel
16、ationships between items within a system or function. These relationships are used by the RBD process in determining serial, parallel, and m of n relationships (out of n components, m are required for success). The architecture of the RBD is attained from a study of the schematics and other diagrams
17、 of the hardware, as well as the ground rules and survival assumptions that dictate which subset of that hardware is to be used. This information is entered into the block diagram editor of the software and is linked to the failure rate data base (into which data must also be hand entered).The secon
18、d type of essential information includes failure rates of the equipment of interest to the lowest modeled level of detail (i.e., piece part, etc.). The third type of essential data is the mission time of each modeled component. The last two pieces of data are used to calculate the reliability for ea
19、ch item in the RBD over the mission time specified and is part of the overall function probability of success.2. Numeric Operations. The core of the RBD analysis is the calculation of the model reliability, usually done with a software tool. The functional relationships, failure data, and mission ti
20、mes are input to the tool and, using user-defined methods, the reliability of the model is calculated. Any number of probability distributions can be used for calculation, with the most common method being the constant failure rate assumption using the exponential distribution. Other distributions c
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