Anomaly Detection Toolkit for Integrated Systems Health Management (ISHM)

Transcription

1 AIAA April 2010, Atlanta, Georgia AIAA Anomaly Detection Toolkit for Integrated Systems Health Management (ISHM) John Schmalzel 1 and Fernando Figueroa 2 NASA, Stennis Space Center, MS, 39529, USA Mark Turowski 3 Jacobs Technology, Stennis Space Center, MS, 39529, USA Richard Franzl 4 Smith Research Corporation, Stennis Space Center, MS, 39529, USA HADS ISHM NASA SSC TEDS VISE Nomenclature = Health Assessment Database System = Integrated Systems Health Management = National Aeronautics and Space Administration = Stennis Space Center = Transducer Electronic Data Sheet = Virtual Intelligent Sensor Environment I. Abstract HE goal of Integrated Systems Health Management (ISHM) is to provide measures of system health using T available data combined with models of behavior. Many technologies contribute to creating an ISHM capability. Arguably the most important of these are methods for anomaly detection, because that is the source of events that trigger downstream reasoning about cause-effect relationships. This paper describes recent efforts to create an anomaly detection toolkit to support on-going ISHM development at NASA-SSC. The toolkit consists of two major components. A health assessment database system (HADS) based on MySQL implements a repository of test data and related configuration data; a companion browser tool allows access to the HADS test data, supports queries, and allows the data to be analyzed using available anomaly detection algorithms. The browser provides a graphical user interface to make the tool usable by operations personnel. II. Introduction NASA s John C. Stennis Space Center (SSC) supports rocket engine testing of components such as turbomachinery and complete engines including the Space Shuttle Main Engine (SSME) and the RS-68. Testing is performed on test stands that include large facilities (B-1/B-2) capable of supporting up to 11 million pounds of thrust in a vertical configuration this is where the Saturn V first stage with 7.5 million pounds of thrust was tested. Intermediate test stands (A-1, A-2) can handle up to 1.1 million pounds of thrust and have been used most recently to test the SSME; these stands are now being refitted to support the J-2X engine. A new stand, A-3, is being completed to support simulated altitude testing of engines on the scale of the J-2X. Smaller test cells (E-1, E-2, E-3) support componentlevel testing and smaller rocket engines. Infrastructure to each test stand provides required oxidizers, fuels, purge gases, cooling water, and other resources at specified pressures, flow rates, and total volumes. Cost-effective operation of the complement of test stands benefits from technologies and methodologies that contribute to reductions in labor costs, improvements in efficiency, reductions in turn-around times, improved reliability, and other measures. Integrated systems health management (ISHM) remains an active area of development at SSC because it offers the potential to achieve many of the cost management goals [1-6]. Core ISHM technologies include smart and intelligent sensors, anomaly detection, root cause analysis, prognosis, and interfaces 1 Innovative Partnerships Office, NASA Stennis Space Center; now with Department of Electrical & Computer Engineering, Rowan University, Glassboro, NJ Innovative Partnerships Office, NASA Stennis Space Center, AIAA Associate Fellow. 3 Engineer, Technology Development and Application, M/S B Engineer, Technology Development and Application, M/S B This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States.

2 to operators and other system elements. Anomaly detection is important because this is the primary source of events that must be reasoned over by other ISHM elements. A. SSC ISHM Architecture The SSC baseline ISHM system architecture consists of the elements shown in Fig. 1. The ISHM system infrastructure consists of a computer and a console located in control room. Data is streamed in real-time from sensors in the test stand. A virtual intelligent sensor environment (VISE) takes the data feed and analyzes it using available anomaly detection algorithms to develop events that are passed on to the ISHM system. The HADS also can access the data for storage purposes. Figure 1. Core elements for the baseline SSC ISHM architecture. B. SSC ISHM Architecture Elements Related to Anomaly Detection The core elements follow directly from the requirements developed for an ISHM system. Some features are common to all test stand environments; for example, each system must collect low-speed (250 Hz) data from facility sensors. This data is then processed in an entity termed a virtual intelligent sensor environment (VISE) prior to submission to the ISHM system. The HADS is also included in the network architecture to allow updating of the test data repository. Summary requirements related to anomaly detection include: 1. Data streamed at 250 Hz into the VISE from low-speed data acquisition system. 2. VISE processing to extract anomaly indicators, including: a. Noise assessment (impulsive, etc.). b. Detection of flat signal (indicator of power loss or saturation). c. Detection of limit violations (sensor operating limits). 2

3 d. Other events (anomaly indicators, anomalies, red/blue limit violations, etc.) and diagnostics to be logged with test-stand time stamp, to indicate what occurred, when, and where within the system. 3. HADS access to low-speed data to allow: a. Incrementally build archival data files from real-time test data. b. Off-line access for data reviews, anomaly detection algorithm development and testing. c. Test data available to other ISHM system elements. III. Anomaly Detection Toolkit Development Goals Our group in conjunction with partners at other NASA Centers, software development groups, universities, and sensor manufacturers has invested significant development efforts in many of the ISHM supporting technology areas. Focus areas include sensors (smart, intelligent), ISHM architecture and system modeling, root cause analysis, and visualization. Areas identified for additional development efforts were improved methods for handling the large amounts of data associated with repetitive testing and creation of companion tools to provide access to data and integration of methods to analyze data using a suite of anomaly detection algorithms. An internal R&D project was initiated to address those needs. The objectives of the effort included: 1. Develop a database structure that helps organize data from archived test files to make them accessible to available anomaly detection algorithms and to support development and testing of new algorithms. 2. Integration of a subset of anomaly detection & sensor validation algorithms into a simple tool to automate post-test data reviews. 3. Evaluation of anomaly detection performance on historical data sets. IV. Results 1. Health Assessment Database System (HADS) HADS is implemented as a MySQL database. The HADS is a repository for test measurements and electronic data sheets including Transducer Electronic Data Sheet (TEDS) data as defined by IEEE STD [7]. We have generalized the TEDS concept and extended it to non-sensor elements e.g., Component Electronic Data Sheets (CEDS) describe attributes of system elements such as valves and piping. Similarly, Health Electronic Data Sheets (HEDS) is a data structure containing parameters needed by anomaly detection algorithms such as filter coefficients, etc. Electronic data sheets are important to minimize re-entering of data as systems are reconfigured and maintained. Figure 2 depicts the relationship diagram for the database. Figure 2. HADS database entity relationship diagram. 2. HADS Browser Application The browser application is a tool for analysis encompassing information stored in the HADS. For instance, a user can select a particular channel from a particular test to visualize its data and if noteworthy features are detected, that same channel can be called up from other tests for comparison. The browser was developed in LabView [8]. The main functions of the browser include: 3

4 1. Support for testing of the new HADS database architecture and facilities for prototyping design features that will be available in the ISHM systems deployed. Figure 3 shows an example output screen showing overlays of several data channels. Figure 3. HADS browser showing multiple channel overlays. 2. Post test analysis of data obtained from routine test programs with ability to apply anomaly detection, trending analysis, etc. This is a key capability of the anomaly detection toolkit effort because it supports flexible access to large amounts of archival test data to allow what if investigations into failure trends and allows testing of new anomaly detection algorithms. Figure 4 shows a thermocouple channel that exhibits noise. Figure 4. HADS browser showing anomalous data for a thermocouple. 3. Electronic data sheet manipulation. The HADS browser supports reading and writing of TEDS and related electronic data sheets. This allows an operator to conveniently access stored descriptions of important transducer parameters such as manufacturer, serial number, calibration due dates, calibration coefficients, etc. An example TEDS browser window is shown in Fig. 5. 4

5 Figure 5. HADS browser window showing the TEDS for a pressure transducer. 4. The HADS browser provides several mechanisms for applying anomaly detection algorithms to the data. Methods that have been implemented to date include the set of procedures previously developed as part of the VISE event detection capabilities, and export to MATLAB [9] procedures. Figure 6 shows an example data channel that exhibits multiple anomalies including exceeding low limits, the presence of flat line segments, and impulsive noise. 5

6 Figure 6. Data channel exhibiting flat line, low threshold, and impulsive noise. V. Conclusions and Future Work This paper summarizes the development of a toolkit that supports management of large data sets taken from rocket engine test stands and provides data visualization and analysis using anomaly detection algorithms. Further development will make the system more processor and storage efficient and will extend the toolkit to the ISHM systems currently under development. References 1. F. Figueroa, J. Schmalzel, M. Walker, M. Venkatesh, R. Kapadia, J. Morris, M. Turowski, and H. Smith, Integrated System Health Management: Foundational Concepts, Approach, and Implementation, AIAA , AIAA Infotech@Aerospace, 6-9 April 2009, Sheraton Seattle Hotel, Seattle, Washington. 2. F. Figueroa and J. Schmalzel, Rocket Testing and Integrated System Health Management, Condition Monitoring and Control for Intelligent Manufacturing, edited by L. Wang and R. Gao, Springer Series in Advanced Manufacturing, Springer Verlag, UK, 2006, pp F. Figueroa, R. Holland, and D. Coote, NASA Stennis Space Center Integrated System Health Management Test Bed and Development Capabilities, SPIE Defense & Security Symposium, Sensors for Propulsion Measurements Applications, April F. Figueroa, R. Holland, J. Schmalzel, and D. Duncavage, Integrated System Health Management (ISHM): Systematic Capability Implementation, Sensors Applications Symposium, 2006, Proceedings of the 2006 IEEE, 7-9 February 2006, pp K. Reichard, F. Figueroa, R. Oosdyke, J. Schmalzel, and J. Perotti, An ISHM Architecture for Ground Operations Health Management, ISHM Conference, Covington Convention Center, KY, August, J. Schmalzel, F. Figueroa, J. Morris, S. Mandayam, and R. Polikar, An Architecture for Intelligent Systems Based on Smart Sensors, IEEE Transactions on Instrumentation and Measurement, Vol. 54, No. 4, August 2005, pp IEEE , Standard for Smart Transducer Interface for Sensors and Actuators Mixed-Mode Communication Protocols and Transducer Electronic Data Sheet (TEDS) Formats, IEEE Standards Association, Piscataway, NJ. 8. National Instruments Corporation, Austin, TX MathWorks, Natick, MA