IEEE Std 1636-2018 pdf download – IEEE Standard for Software Interface for Maintenance Information Collection and Analysis (SIMICA).
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 systerms where it resides. The idea is to identify the information of interest, and to create an OWL ontology so that it is clear what that data is and what it means in the context of system usage and analysis, and then determine the process for obtaining that information. Once data requirements and availability are determined, one can then begin to integrate the heterogeneous information to be obtained by relating the vital information from any one system to the others that capture related information but using disparate data formats and semantics. Data collection is a prerequisite for the maturation process, and the product data that is typically required for maturation analysis is generally stored in heterogeneous data systems. The SIMICA family of standards facilitates improved access, retrieval, and integration of the requisite information from these data systems. The intent of the SIMICA family of standards is to provide an implementation-independent specification for an interface to information systems containing data pertinent to the diagnosis and maintenance of complex systems consisting of hardware, software, or any combination thereof. These interfaces will support the creation of service definitions for application programming interfaces (API) for the access, exchange, and analysis of historical diagnostic and maintenance information. The use of OWL ontologies (in previous version of this standard formal information models based on EXPRESS were used) facilitates exchanging historical maintenance information between information systems and analysis tools, supporting the creation of open system sofware architectures for maturing system diagnostics.