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SBIR/STTR

Advanced Data Mining and Deployment for Integrated Vehicle Health Management and the Space Vehicle Lifecycle, Phase II

Completed Technology Project

Project Introduction

Advanced Data Mining and Deployment for Integrated Vehicle Health Management and the Space Vehicle Lifecycle, Phase II
In a successful Phase 1 project for NASA SBIR topic A1.05, "Data Mining for Integrated Vehicle Health Management," Michigan Aerospace Corporation (MAC) demonstrated its SPADE anomaly detection software to key personnel in NASA's Intelligent Systems Division (ISD) and with data from our partners at Boeing, SpaceX and GMV Space Systems. The feedback from these demonstrations was used to establish future development directions for Phase 2. Phase 2 will consist of three major efforts: 1) the design and implementation of the Taiga system, a next-generation enhancement of the SPADE software, 2) an investigation into combining complementary functionality of Taiga with existing code at ISD including the Inductive Modeling System, Mariana and others, and 3) the implementation of a prototype automatic parallelizer, in cooperation with subcontractor Optillel Solutions, for a subset of C++ useful for hardware acceleration of machine learning applications. The scope of the interaction with researchers in NASA ISD will be to explore the relationships between IMS and Taiga and gauge benefits such a Data Handling, Feature Reduction, Visualization and Explainability. We will also investigate heterogeneous ensemble methods by analyzing the Mariana system. Optillel's C++ Parallelizer will reduce MAC's development costs for parallelizing C++ code for multi-core chips and clusters. This effort will build on Optillel's existing body of work that supports graphical programming languages, and will extend their technology to the analysis and parallelization of C++ code. Both the Taiga system and Optillel's prototype have significant commercialization potential in industries as diverse as Chemical, Pharmaceutical, Manufacturing and Aerospace. More »

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This is a historic project that was completed before the creation of TechPort on October 1, 2012. Available data has been included. This record may contain less data than currently active projects.

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