NASA researchers recently demonstrated successful real-time fault detection and isolation of a virtual reusable launch vehicle main propulsion system. Using a detailed simulation of a vehicle propulsion system to produce synthesized sensor readings, the NASA team demonstrated that advanced diagnostic algorithms, running on flight-like computers, can, in real time, successfully diagnose the presence and cause of faults. This demonstration was conducted as part of the NASA Propulsion IVHM Technology Experiment, or PITEX.

The Propulsion IVHM Technology Experiment (PITEX) has been an on-going research effort conducted over several years. PITEX has developed and applied a model-based diagnostic system for the main propulsion system of the X-34 reusable launch vehicle, a space-launch technology demonstrator. The application was simulation-based using detailed models of the propulsion subsystem to generate nominal and failure scenarios during captive carry, which is the most safety-critical portion of the X-34 flight. Since no system-level testing of the X-34 Main Propulsion System (MPS) was performed, these simulated data were used to verify and validate the software system. Advanced diagnostic and signal processing algorithms were developed and tested in real-time on flight-like hardware. In an attempt to expose potential performance problems, these PITEX algorithms were subject to numerous real-world effects in the simulated data including noise, sensor resolution, command/valve talkback information, and nominal build variations. The current research has demonstrated the potential benefits of model-based diagnostics, defined the performance metrics required to evaluate the diagnostic system, and studied the impact of real-world challenges encountered when monitoring propulsion subsystems.

Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. Inductive Monitoring System uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing Inductive Monitoring System space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the Inductive Monitoring System data-driven technique as related to several Integrated Systems Health Management elements. Thematically, the projects listed will be used as case studies. The maturation of Inductive Monitoring System via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how Inductive Monitoring System can be used to complement a suite of other Integrated System Health Management tools, providing initial fault detection support for diagnosis and recovery.

The space shuttle main engine (SSME) is part of the Main Propnlsion System (MPS) which is an extremely complex system containing several sub-systems and components, each of which must work precisely in order to achieve a successful mission. A critical component under study is the flow control valve (FCV) which controls the pressure of the gaseous hydrogen between the SSME and the external fuel tank. The FCV has received added attention since a Space Shuttle Mission in November 2008, where it was discovered during the mission that an anomaly had occurred in one of the three FCV's. Subsequent inspection revealed that one FCV cracked during ascent. This type of fault is of high criticality because it can lead to potentially catastrophic gaseous hydrogen leakage. A supervised learning method known as Virtual Sensors (VS), and an unsupervised learning method known as the Inductive Monitoring System (IMS) were used to detect anomalies related to the FCV in the MPS. Both algorithms identify the time of the anomaly in a multi-dimensional time series of temperatures, pressures, and control signals related to the FCV. This discovery corroborates the results of the inspection and also reveals the time at which the anomaly likely occurred. The methods were applied to data obtained from the March 2009 launch of Space Shuttle Discovery to determine whether an anomaly occurred in the same sub-system. According to our models, the FCV SUb-system showed nominal behavior during ascent.

# Summary There is a major thrust worldwide on developing affordable Integrated Vehicle Health Management (IVHM) technologies in aerospace, automotive and other areas. The fundamental objective is to enhance availability, increase safety and reduce unscheduled maintenance costs. IVHM enables fault detection, isolation, root cause analysis and potential diagnostics. In addition, it aims at developing robust algorithms to predict the onset of a fault, minimizing false alarms and estimating the remaining useful life of the mission despite the adversity. IVHM optimally integrates technologies in sensors, vehicle systems, prognostics and diagnostics. NASA and CSIR-NAL have received an award to jointly organize the first Indo-US Workshop on IVHM and Aviation Safety (WIAS), sponsored by Indo-US Science and Technology Forum (IUSSTF) from Jan. 5th to 7th, 2012 in Bangalore, which is the aerospace hub of the country. # Purpose The purpose of the workshop is to deliberate, discuss and evolve the state of the art aerospace systems’ health management strategies, and identify opportunities for collaboration between US & Indian Institutions. This will help orchestrate preparation of IVHM roadmap into the future. This is an attempt, initiated at NAL, in the direction of creating an ecosystem among R&D, Academics and Industry on the subject matter as a part of the IVHM Mission for aerospace industry. We thus see unprecedented opportunities for discussions and knowledge networking in the areas of IVHM. # Participation An active participation from following organizations / agencies is expected: Leading US academic institutions including Stanford, Berkley, Georgia Tech and Auburn University US Industry including GM, GE, Honeywell, Boeing Research, Lockheed Martin, Rockwell Collins European organizations such as the Center of Excellence, Cranfield, UK; LMS International, Belgium; Airbus & Dassault systems, France Top Indian Academia like IITs & IISc, R&D (like DRDO, CSIR, DST, DOS), Industry, Regulatory (DGCA) and Armed Forces. The IVHM program envisages close cooperation of R&D, Academia and Industry (national and international) and has immediate applications to legacy, current and future generation aircraft and other programs. # Expected outcome,

This paper describes how damage propagation can be tracked and modeled for a range of fault modes in some modules of commercial high bypass aircraft engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine (cycle deck) as a function of variations of flow and efficiency of the modules of interest. These surfaces are normalized and superimposed. Next, sensor readings are matched to those surfaces and – using an optimization approach – the corresponding flow and efficiency pair is found that best explains the sensor data. This flow and efficiency pair is then compared to previous pairs and the direction of the change as well as the rate of change is determined. The whole trajectory is then projected into the time domain. An extrapolation of the curve to the limit (which is established via operational margins) yields the remaining life. In a backward mode, the extrapolated curve is discretized and estimated future flow and efficiency pairs are retrieved. These pairs are then input to the cycle deck to produce future anticipated sensor readings as well as confirmatory trips of operational margins. Changes of the future sensor readings with real readings are used to adjust the remaining life calculations. The method is demonstrated on time series of historical engine faults.

This paper provides a review of three different advanced machine learning algorithms for anomaly detection in continuous data streams from a ground-test firing of a subscale Solid Rocket Motor (SRM). This study compares Orca, one-class support vector machines, and the Inductive Monitoring System (IMS) for anomaly detection on the data streams. We measure the performance of the algorithm with respect to the detection horizon for situations where fault information is available. These algorithms have been also studied by the present authors (and other co-authors) as applied to liquid propulsion systems. The trade space will be explored between these algorithms for both types of propulsion systems.

Title: Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring. Abstract: This article describes the results of applying four unsupervised anomaly detection algorithms to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The article describes nine anomalies detected by the four algorithms. The four algorithms use four different definitions of anomalousness. Orca uses a nearest-neighbor approach, defining a point to be an anomaly if its nearest neighbors in the data space are far away from it. The Inductive Monitoring System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side of a hyperplane, and then classify points on the other side of the hyperplane as anomalous. Because of these different definitions of anomalousness, different algorithms detect different anomalies. We therefore conclude that it is useful to use multiple algorithms.

The Optical Plume Anomaly Detection (OPAD) system is under development to predict engine anomalies and engine parameters of the Space Shuttle's Main Engine (SSME). The anomaly detection is based on abnormal metal concentrations in the optical spectrum of the rocket plume. Such abnormalities could be indicative of engine corrosion or other malfunctions. Here, we focus on the second task of the OPAD system, namely the prediction of engine parameters such as rated power level (RPL) and mixture ratio (MR). Because of the high dimensionality of the spectrum, we developed a linear algorithm to resolve the optical spectrum of the exhaust plume into a number of separate components, each with a different physical interpretation. These components are used to predict the metal concentrations and engine parameters for online support of ground-level testing of the SSME. Currently, these predictions are labor intensive and cannot be done online. We predict RPL using neural networks and give preliminary results.