Deep Learning Based Real-Time Engine Prognostics and Health Management (DL-RTEPHM)

Testing and verifying rocket engines requires the careful placement, calibration, and analysis of dozens of sensors to measure important system characteristics such as temperature, density, flow, vibration, etc. Due to the volume and complexity of the raw outputs of these sensors, most are not monitored live and require extensive amounts of time from engineers to analyze and verify tests in an offline manner. In recent decades, Machine learning (ML) technologies have been proven to speed up the process and reliability of identifying anomalies. However, as the volume and complexity of sensor data has grown, traditional ML methods have failed to scale and require carefully crafted sensor models to get meaningful results. More recently, Deep Learning (DL) methods have been proven to achieve superhuman performance and reliability in extremely complex domains such as object recognition, natural language processing, and games. They achieve this by identifying patterns and relationships in data that humans are unable to quantify and encoding them hierarchically within “deep” neural networks. With DL technology having been proven in many domains, we will build a DL-based, real-time engine sensor diagnostic and health management system to enable superhuman-level analysis and decision-making during firing tests and launches.

NASA is a major developer and operator of rocket engines. Machine Learning (ML)/Deep learning (DL) can provide great a benefit for automated engine operations, health monitoring, prognostics, fault detection and, remaining useful life predictions for various components. By bringing ML/DL to the area of rocket engine testing and operations, NASA will benefit from the latest development in Artificial Intelligence (AI) and also will transition rocket engine technology to a new era of automation.

The rocket industry serves both NASA the USAF, and commercial launch companies. The proposed DL applications will be transitioned to industry. Other potential users include manufacturing facilities, stationary machinery such as generators, turbines, etc., as well as mobile applications such as automotive engines and airplane engines.

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.