GSSLLC C 2017 ALL RIGHTS RESERVED CLOUD BASED

GSSLLC (C) 2017. ALL RIGHTS RESERVED CLOUD BASED SYSTEM HEALTH RECORD (SHR) PLATFORM FOR CONDITION BASED MAINTENANCE December 19, 2017 Joint Technology Exchange Group (JTEG) Forum-on Improved Business Processes

12/19/2017 GSSLLC (C) 2017. ALL RIGHTS RESERVED 2 Motivation • The need to improve the collection, transformation, exchange and quality of ATE/LRU asset data across the maintenance enterprise: • Enable the acquisition, transformation, integration, analysis and exchange of health • • state data, test results, and maintenance information related to a specific ATE/LRU asset. Collection of the “right data”, derivation of the equipment health indicators, and analysis of the data in critical areas such as asset configuration, maintenance action information and at/off-platform test results are essential for reliable analytics. Without completed fields, properly modeled or validated data, reliable and efficient analysis are difficult. Making investments in IT infrastructure and analytics tools without having solved this critical problem is not cost-effective. Addressing this gap is crucial to effectively draw value from the asset data and deliver the required weapon system readiness –through condition based/predictive maintenance.

12/19/2017 GSSLLC (C) 2017. ALL RIGHTS RESERVED 3 A Solution • The System Health Record (SHR) Platform is designed to consume structured or unstructured data from multiple data sources: • Enables asset connectivity and a common, open information model so that participating organizations (O, I, D, OEM) can perform their specific maintenance activities related to the system (subject) • Enables interoperability, communication, capture and sharing of common fundamental health record data and system functionality. • The “right data” is collected as part of the overall workflow and aggregated through the “persistent” system health record. • The data can be used to enable predictive analytics- improving weapon system readiness, mission planning, fleet management operations, and other business processes.

12/19/2017 GSSLLC (C) 2017. ALL RIGHTS RESERVED 4 System Health Record (SHR™) Platform A cloud based platform on Amazon Web Services (AWS). • Efficient ATE/TPS data collection, transformation, integration, exchange across levels of maintenance using IEEE 1636. 1/2 standards. • Autonomous data ingestion and ETL customization for new data sources. •

12/19/2017 GSSLLC (C) 2017. ALL RIGHTS RESERVED 5 Key SHR Features • At each level of maintenance, the SHR enables visibility of: • The current fault symptoms, past observations, diagnostic results, by serial • • number of the asset. The current health state of the subject (aircraft system, ATE asset or LRU) against a normal operating profile. Correlation between BIT, parametric data, test data and operator/maintainer observations to minimize No-fault-found (NFF) incidents. The ATE tests needed for efficient isolation and treatment of the impending or actual system fault. Bad actors and estimated system performance life remaining (PLR) or time -to-failure derived from predictive analytics Apps.

12/19/2017 GSSLLC (C) 2017. ALL RIGHTS RESERVED 6 Cloud-based (AWS) cyber-secured Apps

12/19/2017 GSSLLC (C) 2017. ALL RIGHTS RESERVED 7 State of Technology • Is initial prototype system (Hadoop) developed to collect O -level (BIT) data, RT-CASS TPS data, and maintenance (Deckplate) information from V-22 LRUs. • Enhanced prototype system( AWS)developed to collect Depot ATE (RT-CASS) data and maintenance (Deckplate) information from V-22 LRUs. • SBIR Phase II. 5 under review by NAVAIR sponsor; develop cyber-secured environment to enable intelligent, autonomous data acquisition, transformation and data transfer using commercial data exchange standards.

12/19/2017 GSSLLC (C) 2017. ALL RIGHTS RESERVED 8 Challenges • Data Ownership: • OEM dependence & data ownership issues. • Programs and Maintenance organizations limit access to their data. • “Perceived value” and “classification” of raw asset data maintenance information limit rapid dissemination across the enterprise. • Connectivity: • Non-integrated data supply chain. • At/off-platform test and maintenance information is still captured into paper records or within isolated databases. • Maintenance and logistics information systems may limit the ability of users to extract relevant data in a form that can be communicated to other such system. • Data Quality • Dissimilar preservation of data source context (Values, Meta Data) • The data collected across operations and maintenance activities is in numerous non -standard formats

12/19/2017 9 9 Discussion Contact Information: Luis Hernandez Managing Director 703 -466 -0500, X 2002 luis@gssllc. net