Smart and Intelligent Sensors

John C. Stennis Space Center (SSC) provides rocket engine propulsion testing for NASA's space programs. Since the development of the Space Shuttle, every Space Shuttle Main Engine (SSME) has undergone acceptance testing at SSC before going to Kennedy Space Center (KSC) for integration into the Space Shuttle. The SSME is a large cryogenic rocket engine that uses Liquid Hydrogen (LH2) as the fuel. As NASA moves to the new ARES V launch system, the main engines on the new vehicle, as well as the upper stage engine, are currently base lined to be cryogenic rocket engines that will also use LH2. The main rocket engines for the ARES V will be larger than the SSME, while the upper stage engine will be approximately half that size. As a result, significant quantities of hydrogen will be required during the development, testing, and operation of these rocket engines.Better approaches are needed to simplify sensor integration and help reduce life-cycle costs. 1.Smarter sensors. Sensor integration should be a matter of "plug-and-play" making sensors easier to add to a system. Sensors that implement new standards can help address this problem; for example, IEEE STD 1451.4 defines transducer electronic data sheet (TEDS) templates for commonly used sensors such as bridge elements and thermocouples. When a 1451.4 compliant smart sensor is connected to a system that can read the TEDS memory, all information needed to configure the data acquisition system can be uploaded. This reduces the amount of labor required and helps minimize configuration errors. 2.Intelligent sensors. Data received from a sensor be scaled, linearized; and converted to engineering units. Methods to reduce sensor processing overhead at the application node are needed. Smart sensors using low-cost microprocessors with integral data acquisition and communication support offer the means to add these capabilities. Once a processor is embedded, other features can be added; for example, intelligent sensors can make a health assessment to inform the data acquisition client when sensor performance is suspect. 3.Distributed sample synchronization. Networks of sensors require new ways for synchronizing samples. Standards that address the distributed timing problem (for example, IEEE STD 1588) provide the means to aggregate samples from many distributed smart sensors with sub-microsecond accuracy. 4. Reduction in interconnect. Alternative means are needed to reduce the frequent problems associated with cabling and connectors. Wireless technologies offer the promise of reducing interconnects and simultaneously making it easy to quickly add a sensor to a system

A Venture Capital Fund for Undergraduate Engineering Students at Rowan University

All engineering students at Rowan University are required to take the 8-semester Engineering Clinic sequence wherein multidisciplinary student teams engage in semester-long design projects. In addition to projects that are funded by local industry, faculty research grants or departmental budgets, a Venture Capital Fund has been created, which is specifically ear-marked for the development of original student inventions. Funding of up to $2500 per student team per semester is competitively awarded based on student-generated proposals to the Venture Capital Fund, which has been created through a series of grants from the National Collegiate Inventors and Innovators Alliance (NCIIA). To qualify for funding, a multidisciplinary student team must propose, plan and implement an original, semester-long product development enterprise. To date, eleven projects have been funded through the Venture Capital Fund. This paper describes the results of several student entrepreneurial projects and compares the results of student surveys to assess the effectiveness of entrepreneurial projects in satisfying the technical objectives of the Engineering Clinic. The results suggest that students engaged in entrepreneurial projects devote more hours per week on their projects, have more “ownership” in their projects, and have a better understanding of the technical aspects and societal impact of their projects than their counterparts who are engaged in the more traditional engineering design projects

Intelligent Sensors for Integrated Systems Health Management (ISHM)

IEEE 1451 Smart Sensors contribute to a number of ISHM goals including cost reduction achieved through: a) Improved configuration management (TEDS); and b) Plug-and-play re-configuration. Intelligent Sensors are adaptation of Smart Sensors to include ISHM algorithms; this offers further benefits: a) Sensor validation. b) Confidence assessment of measurement, and c) Distributed ISHM processing. Space-qualified intelligent sensors are possible a) Size, mass, power constraints. b) Bus structure/protocol

Creating an Entrepreneurial Culture at a Startup Engineering Program

In 1992, the College of Engineering at Rowan University was created as the direct result of a $100 million gift from entrepreneur Henry M. Rowan. Mr. Rowan’s requirements were that the gift be used to create a high-quality, public undergraduate engineering institution and to impact the economic development of southern New Jersey, a region which has historically lagged behind northern New Jersey. Having started with a clean curriculum slate during a period of national change in engineering curricula in response to ABET 2000, we had the opportunity to infuse an entrepreneurial culture into our engineering program from its inception. Specifically, we have developed the following policies/programs: • Created an 8-semester Engineering Clinic course sequence in which hands-on design projects are completed every semester. • Developed a “job-fair” model for student clinic project staffing in which students get “hired” into their Engineering Clinic projects by marketing themselves and their capabilities to faculty, • Created an Undergraduate Venture Capital Fund where students can obtain funding up to$2500 per semester to develop their own original inventions, • Created the Competitive Assessment Laboratory for competitive benchmarking of consumer products. • Developed a micro-business model in which some Engineering Clinic project teams provide engineering services (design, fabrication, modeling, etc.) to other projects, • Hired (College of Business) an endowed chair in entrepreneurial studies, • Created the Technological Entrepreneurship Concentration, which is a certificate program that will be populated jointly by Engineering and Business students, • Obtained state funding to build the South Jersey Technology Park and Technology Business Incubator adjacent to the Rowan campus. This paper will describe the impact of each of these initiatives toward creating an entrepreneurial culture in our undergraduate students. It should be noted that many of these initiatives do not require a new program or major curriculum reform. Rather, our results suggest that it is possible to start with some small initiatives and build upon each initiative as the momentum for entrepreneurship develops

A Virtual Reality Environment for Multi-Sensor Data Integration

Virtual reality (VR) has typically found applications in industrial design, rapid prototyping and advanced scientific visualization. In this paper, we investigate the use of VR for multi-sensor data integration. We attempt to demonstrate that multiple data types-graphical, functional and measurement can be effectively combined inside of a VR environment. This platform allows the user to rapidly sift through large and complex data sets and isolate features of interest. Furthermore, VR environments can be made to evolve based on system data and user input-this provides the ability to develop scenarios that can be used to make informed decisions. Results demonstrating the effectiveness of this approach are shown using the example of multi-sensor gas transmission pipeline inspection. This work is supported in part by the National Science Foundation award #0216348

ISHM Implementation for Constellation Systems

Integrated System Health Management (ISHM) is a capability that focuses on determining the condition (health) of every element in a complex System (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK) "not just data" to control systems for safe and effective operation. This capability is currently done by large teams of people, primarily from ground, but needs to be embedded on-board systems to a higher degree to enable NASA's new Exploration Mission (long term travel and stay in space), while increasing safety and decreasing life cycle costs of systems (vehicles; platforms; bases or outposts; and ground test, launch, and processing operations). This viewgraph presentation reviews the use of ISHM for the Constellation system

Systems Modeling to Implement Integrated System Health Management Capability

ISHM capability includes: detection of anomalies, diagnosis of causes of anomalies, prediction of future anomalies, and user interfaces that enable integrated awareness (past, present, and future) by users. This is achieved by focused management of data, information and knowledge (DIaK) that will likely be distributed across networks. Management of DIaK implies storage, sharing (timely availability), maintaining, evolving, and processing. Processing of DIaK encapsulates strategies, methodologies, algorithms, etc. focused on achieving high ISHM Functional Capability Level (FCL). High FCL means a high degree of success in detecting anomalies, diagnosing causes, predicting future anomalies, and enabling health integrated awareness by the user. A model that enables ISHM capability, and hence, DIaK management, is denominated the ISHM Model of the System (IMS). We describe aspects of the IMS that focus on processing of DIaK. Strategies, methodologies, and algorithms require proper context. We describe an approach to define and use contexts, implementation in an object-oriented software environment (G2), and validation using actual test data from a methane thruster test program at NASA SSC. Context is linked to existence of relationships among elements of a system. For example, the context to use a strategy to detect leak is to identify closed subsystems (e.g. bounded by closed valves and by tanks) that include pressure sensors, and check if the pressure is changing. We call these subsystems Pressurizable Subsystems. If pressure changes are detected, then all members of the closed subsystem become suspect of leakage. In this case, the context is defined by identifying a subsystem that is suitable for applying a strategy. Contexts are defined in many ways. Often, a context is defined by relationships of function (e.g. liquid flow, maintaining pressure, etc.), form (e.g. part of the same component, connected to other components, etc.), or space (e.g. physically close, touching the same common element, etc.). The context might be defined dynamically (if conditions for the context appear and disappear dynamically) or statically. Although this approach is akin to case-based reasoning, we are implementing it using a software environment that embodies tools to define and manage relationships (of any nature) among objects in a very intuitive manner. Context for higher level inferences (that use detected anomalies or events), primarily for diagnosis and prognosis, are related to causal relationships. This is useful to develop root-cause analysis trees showing an event linked to its possible causes and effects. The innovation pertaining to RCA trees encompasses use of previously defined subsystems as well as individual elements in the tree. This approach allows more powerful implementations of RCA capability in object-oriented environments. For example, if a pressurizable subsystem is leaking, its root-cause representation within an RCA tree will show that the cause is that all elements of that subsystem are suspect of leak. Such a tree would apply to all instances of leak-events detected and all elements in all pressurizable subsystems in the system. Example subsystems in our environment to build IMS include: Pressurizable Subsystem, Fluid-Fill Subsystem, Flow-Thru-Valve Subsystem, and Fluid Supply Subsystem. The software environment for IMS is designed to potentially allow definition of any relationship suitable to create a context to achieve ISHM capability

Wireless Bus Interconnects for Flexible and Reliable CubeSat Signal Integrations

One of the largest, yet easily forgotten, aspects of constructing any complex system is the effort needed to integrate several subsystems. One common way to do this is to standardize the interface between subsystems, whether that is a physical standard, such as USB, or protocol standards, such as Wi-Fi and Bluetooth. In our previous implementations of CubeSat systems and subsystems we have found the PC/104 bus to be volume and mass inefficient while allowing too many potential conflicts to be considered a \u27standard\u27. Our research proposes to implement a wireless, Bluetooth based, communication interface between subsystems to minimize the physical and logistical effort required to build CubeSat systems. By completely removing the need for physical/electrical connections between the subsystems, the barrier to entry for making custom modules for CubeSats can be lowered dramatically. Throughout the course of this research, the applicability of Bluetooth Low-Energy (BLE) utilizing the Generic Attribute (GATT) protocol is investigated

Integrated System Health Management (ISHM) for Test Stand and J-2X Engine: Core Implementation

ISHM capability enables a system to detect anomalies, determine causes and effects, predict future anomalies, and provides an integrated awareness of the health of the system to users (operators, customers, management, etc.). NASA Stennis Space Center, NASA Ames Research Center, and Pratt & Whitney Rocketdyne have implemented a core ISHM capability that encompasses the A1 Test Stand and the J-2X Engine. The implementation incorporates all aspects of ISHM; from anomaly detection (e.g. leaks) to root-cause-analysis based on failure mode and effects analysis (FMEA), to a user interface for an integrated visualization of the health of the system (Test Stand and Engine). The implementation provides a low functional capability level (FCL) in that it is populated with few algorithms and approaches for anomaly detection, and root-cause trees from a limited FMEA effort. However, it is a demonstration of a credible ISHM capability, and it is inherently designed for continuous and systematic augmentation of the capability. The ISHM capability is grounded on an integrating software environment used to create an ISHM model of the system. The ISHM model follows an object-oriented approach: includes all elements of the system (from schematics) and provides for compartmentalized storage of information associated with each element. For instance, a sensor object contains a transducer electronic data sheet (TEDS) with information that might be used by algorithms and approaches for anomaly detection, diagnostics, etc. Similarly, a component, such as a tank, contains a Component Electronic Data Sheet (CEDS). Each element also includes a Health Electronic Data Sheet (HEDS) that contains health-related information such as anomalies and health state. Some practical aspects of the implementation include: (1) near real-time data flow from the test stand data acquisition system through the ISHM model, for near real-time detection of anomalies and diagnostics, (2) insertion of the J-2X predictive model providing predicted sensor values for comparison with measured values and use in anomaly detection and diagnostics, and (3) insertion of third-party anomaly detection algorithms into the integrated ISHM model

Smart Sensor Demonstration Payload

Sensors are a critical element to any monitoring, control, and evaluation processes such as those needed to support ground based testing for rocket engine test. Sensor applications involve tens to thousands of sensors; their reliable performance is critical to achieving overall system goals. Many figures of merit are used to describe and evaluate sensor characteristics; for example, sensitivity and linearity. In addition, sensor selection must satisfy many trade-offs among system engineering (SE) requirements to best integrate sensors into complex systems [1]. These SE trades include the familiar constraints of power, signal conditioning, cabling, reliability, and mass, and now include considerations such as spectrum allocation and interference for wireless sensors. Our group at NASA s John C. Stennis Space Center (SSC) works in the broad area of integrated systems health management (ISHM). Core ISHM technologies include smart and intelligent sensors, anomaly detection, root cause analysis, prognosis, and interfaces to operators and other system elements [2]. Sensor technologies are the base fabric that feed data and health information to higher layers. 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. ISHM is an active area of development at SSC because it offers the potential to achieve many of those operational goals [3-5]