A report describes analysis of space shuttle main engine (SSME) sensor data using Beacon-based Exception Analysis for Multimissions (BEAM) [NASA Tech Briefs articles, the two most relevant being "Beacon-Based Exception Analysis for Multimissions" (NPO-20827), Vol. 26, No.9 (September 2002), page 32 and "Integrated Formulation of Beacon- Based Exception Analysis for Multimissions" (NPO-21126), Vol. 27, No. 3 (March 2003), page 74] for automated detection of anomalies. A specific implementation of BEAM, using the Dynamical Invariant Anomaly Detector (DIAD),is used to find anomalies commonly encountered during SSME ground test firings. The DIAD detects anomalies by computing coefficients of an autoregressive model and comparing them to expected values extracted from previous training data. The DIAD was trained using nominal SSME test-firing data. DIAD detected all the major anomalies including blade failures, frozen sense lines, and deactivated sensors. The DIAD was particularly sensitive to anomalies caused by faulty sensors and unexpected transients. The system offers a way to reduce SSME analysis time and cost by automatically indicating specific time periods, signals, and features contributing to each anomaly. The software described here executes on a standard workstation and delivers analyses in seconds, a computing time comparable to or faster than the test duration itself, offering potential for real-time analysis.
This work was done by Michail Zak, Han Park, and Mark James of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com/tsp under the Information Sciences category. NPO-30664
This Brief includes a Technical Support Package (TSP).
Analysis of SSEM Sensor Data Using BEAM
(reference NPO-30664) is currently available for download from the TSP library.
Don't have an account?
Overview
The document presents a technical report on the analysis of Space Shuttle Main Engine (SSME) sensor data using a novel approach called Beacon-based Exception Analysis for Multimissions (BEAM). Developed by researchers at NASA’s Jet Propulsion Laboratory (JPL), this method employs the Dynamical Invariant Anomaly Detector (DIAD) to automate the detection of anomalies during SSME ground test firings.
Traditionally, analyzing SSME data required extensive manual effort, consuming many hours for analysts to review the data. The motivation behind developing the DIAD was to create an automated system that could detect anomalies in near real-time, thereby improving efficiency and reducing costs associated with data analysis.
The DIAD operates by computing coefficients of an autoregressive model based on the sensor data and comparing these coefficients to expected values derived from previous training data. This approach allows the system to identify deviations indicative of anomalies, such as blade failures, frozen sense lines, and deactivated sensors. The DIAD has shown particular sensitivity to issues caused by faulty sensors and unexpected transients, making it a valuable tool for ensuring the reliability of the SSME.
The software implementation of the DIAD runs on standard workstations and is capable of delivering analyses in seconds, which is comparable to or even faster than the duration of the tests themselves. This capability offers the potential for real-time analysis, significantly enhancing the operational efficiency of SSME testing.
The report also references several publications that provide further insights into the BEAM technology and its applications, including conference papers and technical briefs that detail the methodology and results of the research.
Overall, the work conducted by Michail Zak, Han Park, and Mark James represents a significant advancement in the field of anomaly detection for propulsion systems, showcasing how innovative approaches can streamline complex analysis processes and improve the safety and performance of aerospace technologies. The findings underscore the importance of integrating automated systems in engineering practices, particularly in high-stakes environments like space exploration.
