The principle goal of the Network is to facilitate the exchange of students, professors and researchers among the major European technological universities and to participate together under European technological development and training programmes. One of the Network's first and most unique actions concerns the organisation of an intensive course programme given at each member institution during one of two defined periods "Sessions" of the academic year November and March.
Provan, Rockwell Science Center, Thousand Oaks, California Abstract We present a flexible, powerful, easily maintainable diagnostics system that integrates diagnostic development functions into product development tools and processes.
Challenges of Developing Diagnostic Systems for Complex Applications There exists a wide variety of modeling and inference methods for diagnostics, including expert systems, neural networks, and model-based approaches. All of these approaches can represent and analyze the same artifact but from different viewpoints.
Complex systems, such as commercial aircraft, represent a challenge both in modeling and inference.
Next generation improvements in the performance of diagnostic systems will require, at the very least, higher fidelity models, guarantees on model consistency, and reasoning algorithms that are less susceptible to state-space explosion problems. Moreover, analyses of the diagnostic models for complex systems must be correct and exhaustive.
Incomplete analyses using probabilistic arguments become progressively more tenuous for complex systems and unacceptable for safety-critical applications. Powerful and generic modeling paradigms and algorithms that seamlessly serve many analytic needs are key ingredients for developing the next generation diagnostic systems.
Other critical aspects of models are capturing aspects of temporal and dynamic systems, and the ability to scale to complex and non-deterministic scenarios.
We argue that these two technologies can synergistically provide: The ability to model a fault using current design data and thus troubleshoot and repair a failure either a not previously identified or b with no existing maintenance procedures.
The ability to provide diagnostics feedback to design engineering during the design and integration process. A significantly enhanced ability to scale to large complex applications while maintaining accuracy.
The ability to guarantee computational requirements, a priori, based on model fidelity. We organize this paper as follows. The following section summarizes the diagnostic representation, causal networks , and introduces a simple example of causal network modeling.
The next section summarizes Rockwell Collin s avionics diagnostic system, briefly describing each major component of the system.
The final sections provide conclusions and references. A causal network is a graph-based representation that is used for diagnosing failures of a system. Causal networks provide probabilistic, order of magnitude, and symbolic representations.
They also have predictable scaling properties for the eventual embedded runtime diagnostic system. A causal network encodes the causal relations of a system; e.
A causal network is thus a good way of representing the flow of data in such systems, and hence reasoning about the root causes of data flow; e. Figure 2 shows the causal network structure for this simple example. Conversely, if data is not flowing as intended, the causal network can help track down the reason for the breakdowns of correct data flow; e.
We call this diagnosing the faults in the system.
Simple Avionics Example More formally, a causal network specifies the causal relationships among a set V of 2 variables by encoding each variable in V with a node, and encoding the causal influence of V 1 on V 2 by a directed arc from V 1 to V 2. Hence a causal network is a directed graph N,A of nodes N and directed arcs A.
Rx1 Rx2 Rx3 Rx4 Figure 2: Causal Network Structure for Simple Avionics Example The example we have just described covers only hardware, but note that the causal network modeling language allows functional specifications of both software and hardware.
Causal Network Specification Language To specify a causal network model we need to define: Rx1 has associated assumables, OK and broken, and a quantification as follows: In propositional logic, we might write this as: Figure 3 shows the causal network model with the assumables and quantification defined.Managing the resources like Information Technology, Telecommunications and Network Management is same as like building a home.
The more work. Principles of the Telecommunications Industry committed to the Guiding Principles of the Telecommunications Industry Dialogue. We Within the Telecommunications Industry Dialogue Group, we share case studies and challenges from our operating environments with other members.
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This followed the spectacular successes of Japanese industry in the late s in consumer electronics and telecommunications. 39 Those initiatives played a paramount role in facilitating the birth of the modern ICT-industry in Europe.
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