ACHIEVING MISSION ASSURANCE - Raytheon

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PROFILE Catherine Keller was fascinated with the big picture right from the start. In fact, at an early age she wanted to become an astronomer, taking as many classes on the subject as she could. What better way to learn how the world works than to study the most universal topic of all? Her instincts would soon pay off. While Catherine was still at Pomona College in Claremont, Calif., the local division of General Dynamics recruited her to work with some of their missile programs. As it happened, astronomy students were routinely recruited to work on electro-optical sensors. “My advisor was on sabbatical my senior year so I ended up writing my thesis on a solid state physics topic: the use of mercury-cadmium-telluride in infrared detectors. I laugh when I look back on how that worked out; Mercad was considered very exotic then. It was great to get hands-on experience while I was still in school.” After graduation Catherine entered into phase two of her multi-faceted career. She landed a job at Hughes Missile Systems in the San Fernando Valley, a stone’s throw from her home. At the same time, she attended USC as a Hughes Fellow, where she obtained a master’s in electrical engineering in Systems and Communications. “I worked a variety of different assignments in my career, because I always wanted to see the bigger picture,” says Keller. “It took me several years but I finally realized that meant I was a systems engineer at heart, not a designer. I really love making the connection between what the customer needs and what solutions we can offer.” Today Catherine is the 2006 lead for Raytheon’s Systems Engineering (SE) Council, composed of the SE leadership from all of the company's businesses. She is also the technical director for the 900-person Systems Development Center in Space and Airborne Systems Engineering located in El Segundo, Calif. Her responsibilities focus on two primary areas: technology strategy and technical review. When it comes to defining Mission Assurance (MA) for SE, Catherine, a certified Raytheon Six Sigma Expert, again applies her “big picture” philosophy. To her MA means more than just ensuring that the customer’s needs and priorities are accurately represented in the delivered product. “Mission Assurance is an attitude about how we do our work. It has to be part of the engineering culture, the technical conscience on a program.” Raytheon Six Sigma is a trademark of Raytheon Company. 14 2006 ISSUE 1 RAYTHEON TECHNOLOGY TODAY Designing for Health Continued from page 13 failure). The correct sensors, real or virtual (inferring information beyond the specified use), need to be identified to monitor the selected parameters. Therefore, parameters must be selected that are compatible with the maturity of sensor technology. Sensor selection and placement starts with the testability analysis/model. It is used to identify failure modes, their failure effect propagation, signal-to-noise ratios, propagation time and gain. The figure of merit selection step focuses on maximizing detectability and identifiability of the failure mode while minimizing the number of sensors. The optimization step takes all of these factors through a statistical analysis to provide the optimal number of sensors and their placement. The performance assessment step monitors false positives, false negatives and time delay for the purpose of updating the figure of merit weighting for better resolution (detectability and reliability) of the sensing architecture. A test plan is developed with the objective of collecting baseline and fault data for training/validating diagnostic and prognostic algorithms. The HMS testing phase begins during hardware and software integration and verification. The diagnostic/ prognostic features implemented in hardware are tested and verified. Failure propagation is checked to ensure fault paths are correctly modeled. Failure mechanisms identified for prognostics are characterized by inserting faults and monitoring the effects (through the sensors) in simulated expected environments. Historical data is used wherever possible to characterize the system in the environment for a typical intended mission of the system. When historical data is nonexistent, environmental factors and mission scenarios are simulated during testing, where feasible, to fine-tune characteristics with the objective of reducing false alarms and false negatives. Collected data during the testing phase is used to develop and mature diagnostic and prognostic algorithms. Diagnostic and prognostic algorithms are developed during characterization and finetuned throughout the testing phase. A baseline diagnostic/prognostic health curve is established to start the HMS implementation into the system. As data is collected, the curve is updated. The health curve shows the probabilities of wear, present sensor observation and mission scenario; it then fuses these together to provide a trend observation. This trend observation is used to diagnose a failure and/or predict the probability that a failure will occur in a certain mission time frame. A reasoner is developed using the dependency models as a foundation for diagnosing health and/or predicting failures for conditioned-based maintenance (CBM). It reads the sensor data and filters it using statistical and/or intelligent techniques such as Bayesian, Fuzzy Logic, Neural networks, etc. 4 . Pre-processing algorithms are developed to manipulate streams of data by filtering and extracting the features outlined in the product characterization. The data is fused together, associated into categories and correlated while comparing it to the health curve in the modeling and evaluation section. Predictive maintenance decisions are made and reported out through the control logic. Implementation of the reasoner technology can be embedded on-board the operating system or off-board using external support and test equipment. The reasoner provides the health intelligence necessary for use in the production/deployment and operation support phases. Production/Deployment and Operation/Support Phases During the Production/Deployment and Operation/Support phases (see Fielding block in Figure 2), the HMS capability supports Mission Assurance, exceeding the required operational/support requirements and providing sustainment in the most costeffective manner over the life cycle. HMS data collection and maturation is vital in the Production/Deployment and Operation/ Support phases. Environmental factors that may not be comprehended in analysis and testing contribute in various ways to the HMS algorithms. As a result, the product is further characterized and algorithms are fine-tuned during these system operational phases as data is collected and analyzed. A failure, reporting, analysis and corrective action system (FRACAS) is an effective means to collect, analyze and correct any HMS shortcomings. New HMS technology is
inserted into the system through spiral development resulting in continual improvement. Data collection is key to HMS maturation whether it's for newly designed systems or for systems fielded for several years. This process can be used on existing fielded systems by using the field data to characterize the product and improve the design for HMS. A mature data collection system improves HMS architectures for future programs. The HMS capability is fully tested and its usefulness is maximized between the production factory, fielding and depot activity during these phases. An integrated HMS, in a real-time operational mode, is able to leverage the health information to reconfigure or change mission objectives if critical system capability is lost. This results in a positive impact on mission success. Also, the same health data can be relayed realtime to the maintenance facility to prestage spare parts, tools and personnel for each repair level which reduces turnaround time and increases the overall Ao. Summary System readiness is vital to supporting the warfighter’s needs. A robust health management system enables the warfighter and maintainer to quickly access the health of a system through diagnostics and the probable failure impact of a mission through prognostics, increasing probability of mission success. This systems engineering approach to integrated diagnostics and prognostics provides the foundation to supporting the total test environment with traceability of requirements through analysis and implementation while reducing false alarms and CNDs. Raymond Beshears raymondb@raytheon.com Larry Butler lbutler@raytheon.com References 1. DoD 5000.2 Guidebook Life Cycle Framework, A Publication of the Defense Acquisition University, http://akss.dau.mil/dag/welcome.asp 2. Raytheon HMS information repository is located at https://team01.raytheon.com/eRoom/ RayInternalEC_PortalKnowledgeRoom001/Health ManagementSystemsTIG) 3. Bayesian reference: http://www.murrayc.com/ learning/AI/bbn.shtml 4. Neural Networks and Fuzzy Logic reference: https://team01.raytheon.com/eRoom/RayInternalEC_ PortalKnowledgeRoom001/HealthManagementSystems TIG/0_c05 How Systems and Software Engineering Supports Mission Assurance What is Mission Assurance and how can system and software engineering contribute to its success? A significant portion of this issue of technology today has been dedicated to addressing this question. Yet, beyond the detailed technology, it would behoove all of us in the engineering community to reflect momentarily upon those end users of our products. Consider, for example, a soldier who finds himself pinned down by enemy fire. The soldier must eliminate the enemy position, often times with friendly forces in close proximity. The soldier may choose to use a Raytheon product, like the Javelin. Clearly, in such situations the missile must perform without error. The very lives of our armed forces depend upon it. Such a scenario, though fictitious, is realistic and is an excellent example of the importance of Mission Assurance — our products must work as claimed for the warfighter on the ground. Such examples should leave those of us in the engineering community with a desire to truly understand how to achieve “no doubt” results for all Raytheon products. Thus, the very important question: Exactly how does a system achieve Mission Assurance? It is, of course, true that there are many contributing factors — yet we claim that integrated systems and software engineering are among the most important. Systems engineering deals with the fundamental problem of how the “system” must behave and operate; software engineering, ENGINEERING PERSPECTIVE Kenneth Kung PRINCIPAL ENGINEERING FELLOW meanwhile, provides the flexibility to provide the needed features. These two disciplines are at the forefront of ensuring that all Raytheon systems perform consistently in a way that our customers desire and expect. Progressing by Leaps and Bounds System and software engineering (particularly as an integrated discipline) has come a long way over the past several decades. It used to be that mainframe computers and microcomputers were quite unreliable, and multiple system crashes were just an accepted part of your day. As a result, it was very difficult to rely on computers for any lifesafety systems. Case in point: The air traffic control systems that Raytheon delivered in the 1960s relied more on hardware components to provide the overall air picture to controllers and flight strips were physically handed off from one operator to another. Today, operators can perform this same hand-off with just a few clicks. Designing Software-Intensive Systems Today, we have much more mature and complex technologies that allow us to design software-intensive systems. Behind these systems are many software and hardware components that must work with each other. For instance, object-oriented system engineering and Unified Modeling Language allow us to specify and design unambiguously. We’re also in the midst of researching how to better create system of systems, and eventually, systems of elements. Some of the articles in this issue illustrate the advances we’re making. And while progress is certainly being made, there’s no doubt that more work is still needed in system and software engineering. 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