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About the Workshop
# 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,
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Developing IVHM Requirements for Aerospace Systems
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The term Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable sustainable and safe operation of components and subsystems within aerospace platforms. However, very little guidance exists for the systems engineering aspects of design with IVHM in mind. It is probably because of this that designers have to use knowledge picked up exclusively by experience rather than by established process. This motivated a group of leading IVHM practitioners within the aerospace industry under the aegis of SAE’s HM-1 technical committee to author a document that hopes to give working engineers and program managers clear guidance on all the elements of IVHM that they need to consider before designing a system. This proposed recommended practice (ARP6883 [1]) will describe all the steps of requirements generation and management as it applies to IVHM systems, and demonstrate these with a “real-world” example related to designing a landing gear system. The team hopes that this paper and presentation will help start a dialog with the larger aerospace community and that the feedback can be used to improve the ARP and subsequently the practice of IVHM from a systems engineering point-of-view.
Vehicle-Level Reasoning Systems: Integrating System-Wide data to Estimate Instantaneous Health State
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One of the primary goals of Integrated Vehicle Health Management (IVHM) is to detect, diagnose, predict, and mitigate adverse events during the flight of an aircraft, regardless of the subsystem(s) from which the adverse event arises. To properly address this problem, it is critical to develop technologies that can integrate large, heterogeneous (meaning that they contain both continuous and discrete signals), asynchronous data streams from multiple subsystems in order to detect a potential adverse event, diagnose its cause, predict the effect of that event on the remaining useful life of the vehicle, and then take appropriate steps to mitigate the event if warranted. These data streams may have highly non-Gaussian distributions and can also contain discrete signals such as caution and warning messages which exhibit non-stationary and obey arbitrary noise models. At the aircraft level, a Vehicle-Level Reasoning System (VLRS) can be developed to provide aircraft with at least two significant capabilities: improvement of aircraft safety due to enhanced monitoring and reasoning about the aircraft’s health state, and also potential cost savings through Condition Based Maintenance (CBM). Along with the achieving the benefits of CBM, an important challenge facing aviation safety today is safeguarding against system- and component-level failures and malfunctions. Citation: A. N. Srivastava, D. Mylaraswamy, R. Mah, and E. Cooper, “Vehicle Level Reasoning Systems: Concept and Future Directions,” Society of Automotive Engineers Integrated Vehicle Health Management Book, Ian Jennions, Ed., 2011.
IVHM Integrity Assurance
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Integrity Assurance methods for Integrated Vehicle Health Management
Integrated Health Management Definitions
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The Joint Army Navy NASA Air Force Modeling and Simulation Subcommittee's Integrated Health Management panel was started about 6 years ago to help foster communication and collaboration in health management related issues for liquid and solid rocket propulsion systems. The panel is co-chaired by Mr. Scott Hyde (ATK) and Ashok N. Srivastava, Ph.D. (NASA). In order to have a common langauge for health management, we need to have a common set of definitions. We have attached a MS Excel spreadsheet that covers the many terms that are of interest to us in the field. Please take a look at the definitions and provide comments and additional terms (with or without definitions) using the feedback box below. We will compile all the definitions into a master list for submittal to the Modeling and Simulation Subcommittee.
Transient Region Coverage in the Propulsion IVHM Technology Experiment
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Over the last several years researchers at NASA Glenn and Ames Research Centers have developed a real-time fault detection and isolation system for propulsion subsystems of future space vehicles. The Propulsion IVHM Technology Experiment (PITEX), as it is called follows the model-based diagnostic methodology and employs Livingstone, developed at NASA Ames, as its reasoning engine. The system has been tested on flight-like hardware through a series of nominal and fault scenarios. These scenarios have been developed using a highly detailed simulation of the X-34 flight demonstrator main propulsion system and include realistic failures involving valves, regulators, microswitches, and sensors. This paper focuses on one of the recent research and development efforts under PITEX – to provide more complete transient region coverage. It describes the development of the transient monitors, the corresponding modeling methodology, and the interface software responsible for coordinating the flow of information between the quantitative monitors and the qualitative, discrete representation in Livingstone.
An Integrated Model-Based Diagnostic and Prognostic Framework
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Systems health monitoring is essential in guar- anteeing the safe, efficient, and correct opera- tion of complex engineered systems. Diagnosis, which consists of detection, isolation and identi- fication of faults; and prognosis, which consists of prediction of the remaining useful life of com- ponents, subsystems, or systems; constitute sys- tems health monitoring. This paper presents an integrated model-based diagnostic and prognos- tic framework, where we make use of a com- mon modeling paradigm to model both the nom- inal and faulty behavior in all aspects of systems health monitoring. We illustrate our approach us- ing a simulated propellant loading system that in- cludes tanks, valves, and pumps.
The Case for Software Health Management
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Software Health Management (SWHM) is a new field that is concerned with the development of tools and technologies to enable automated detection, diagnosis, prediction, and mitigation of adverse events due to software anomalies. Significant effort has been expended in the last several decades in the development of verification and validation methods for software intensive systems, but it is becoming increasingly more apparent that this is not enough to guarantee that a complex software system meets all safety and reliability requirements. Modern software systems can exhibit a variety of failure modes which can go undetected in a verification and validation process. While standard techniques for error handling, fault detection and isolation can have significant benefits for many systems, it is becoming increasingly evident that new technologies and methods are necessary for the development of techniques to detect, diagnose, predict, and then mitigate the adverse events due to software that has already undergone significant verification and validation procedures. These software faults often arise due to the interaction between the software and the operating environment. Unanticipated environmental changes lead to software anomalies that may have significant impact on the overall success of the mission. Because software is ubiquitous, it is not sufficient that errors are detected only after they occur. Rather, software must be instrumented and monitored for failures before they happen. This prognostic capability will yield safer and more dependable systems for the future. This paper addresses the motivation, needs, and requirements of software health management as a new discipline. Published in the Proceedings of the IEEE Conference on Space Mission Challenges for Information Technology, Palo Alto, CA, August 2011.
An Approach to Prognostic Decision Making in the Aerospace Domain
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The field of Prognostic Health Management (PHM) has been undergoing rapid growth in recent years, with development of increasingly sophisticated techniques for diagnosing faults in system components and estimating fault progression tra- jectories. Research efforts on how to utilize prognostic health information (e.g. for extending the remaining useful life of the system, increasing safety, or maximizing operational ef- fectiveness) are mostly in their early stages, however. This process of using prognostic information to determine a sys- tem’s actions or its configuration is beginning to be referred to as Prognostic Decision Making (PDM). In this paper we, first, propose a formulation of the PDM problem with the at- tributes of the aerospace domain in mind, outline some of the key requirements on PDM methods, and explore techniques that can be used as a foundation of PDM development. The problem of Pareto set viability, i.e. satisfaction of perfor- mance goals set for objective functions, is discussed next, followed by ideas for possible solutions. The ideas, termed Dynamic Constraint Redesign (DCR), have roots in the fields of Multidisciplinary Design Optimization and Game Theory. Prototype PDM and DCR algorithms are also described and results of their testing are presented.
Propulsion IVHM Technology Experiment Overview
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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.
Integrated Fault Diagnostics of Networks and IT Systems
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The lecture of the [Stanford-IVHM lecture series](https://dashlink.arc.nasa.gov/group/stanford-ivhm-lecture-series/) will give an overview of the approaches in building diagnostic solutions for networks and complex systems. The conventional rule-based approach and the top-down analysis will be compared with other innovative solutions based on information modeling and codebook correlation. One specific solution pioneered by research done in Columbia University and later implemented by SMARTS/EMC will be presented in more detail as an example of a consistent approach to diagnostics. Speaker: Yuri Rabover, Ph.D. VMTurbo Dr. Yuri Rabover, is a co-founder and Director of Product Strategy of VMTurbo, a startup in a stealth mode. Prior to VMTurbo Yuri spent 12 years working for SMARTS as director of engineering, product management and technology partnership. After EMC acquired SMARTS for $275M in 2005, Yuri was managing the Advanced Solution Group in the EMC Corporate CTO Office developing prototypes and proof of concepts of new innovative solutions. He is a seasoned technologist, strategist and researcher in the wide area of system, network and storage management with more than 20 years of industry and academia experience.
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