IEEE 1636:2009 pdf free download

IEEE 1636:2009 pdf free download.IEEE Trial-Use Standard for Software Interface for Maintenance Information Collection and Analysis (SIMICA).
4. Backgrounds
4.1 Diagnostic maturation
The process ol maturing a system diagnosuc uesign begins in me systems conceptuat design phase ana continues throughout the system life cycle. Generally diagnostic design arc demonstrated prior to dclivcry of the first unit to med the diagnostic and health management requirements that have been levied. However, once a system is fielded and used in an operational environment, unexpected and unplanned system level design interactions, operational and environmental stresses, and other influences tend to reveal deficiencies in the diagnostic capabilities. When such deficiencies result in a weapon system readiness or cost of ownership problem, remedial actions must be taken:
— The performance issue must be clearly characterized in terms of scope. impact. and ownership.
— Root cause analysis must be performed to identify potential courses of actions, including adjustments. improvements, or refinements to support and operational elements.
— The resulting courses of action must be deployed in a manner that is both economically feasible and consistent with product functional and operational requirements.
The diagnostic maturation process requires access to design. maintenance, and logistics support information sources. Aggregation of system failure and performance data from disparate information sources is a multidisciplinary issue, i.e.. relevant data exists within the purview of engineering, product support, and other organizations. Each of these organizations is also multifaceted in nature. Engineering organizations, for example, are composed of sub-disciplines such as design, reliability and maintainability. etc. Compounding the problem is the fact that the data of interest resides in multiple systems each with different owners where it does exist and it should be recognized that some data that is desirable to have might not be captured in data systems at all.
The problem can be reduced by operating with a single. unified conceptual view of the data to be extracted from multiple sources. Therefore, the challenge is how to extract the information of interest
heterogeneous system failure and performance data—from the disparate data systems where it resides. The idea is to identify the information of interest—and in fact, to create an information model so that it is clear what that data is and what it really means in the context of system usage and analysis then determine the process for obtaining that information. Once data requirements and availability are determined, one can then begin trying to solve the puzzle of how to integrate the heterogeneous information to be obtained. i.e.. how to relate the vital information from any one system to the others that capture related information but using disparate data formats and semantics.