3,554 research outputs found
A probabilistic model for information and sensor validation
This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is only apparent since it may be that the estimated value is itself based on faulty data. The theory extends our understanding of when it is possible to isolate real faults from potential faults and supports the development of an algorithm that is capable of isolating real faults without deferring the problem to the use of expert provided domain-specific rules. To enable practical adoption for real-time processes, an any time version of the algorithm is developed, that, unlike most other algorithms, is capable of returning improving assessments of the validity of the sensors as it accumulates more evidence with time. The developed model is tested by applying it to the validation of temperature sensors during the start-up phase of a gas turbine when conditions are not stable; a problem that is known to be challenging. The paper concludes with a discussion of the practical applicability and scalability of the model
Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox
The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better condition monitoring and preventative maintenance strategies these costs could be reduced. In this paper an on-line condition monitoring system is created that is capable of diagnosing machine component conditions based on an array of sensor readings. It then informs the operator of actions required. This simplifies the role of the operator and the actions required can be optimised within the program to minimise costs. The program has been applied to a gearbox oil testbed to demonstrate its operational suitability. In addition a method for determining the most cost effective maintenance strategy is examined. This method uses a Dynamic Bayesian Network to simulate the degradation of wind turbine components, effectively acting as a prognostics tool, and calculates the cost of various preventative maintenance strategies compared to purely corrective maintenance actions. These methods are shown to reduce the cost of operating wind turbines in the offshore environment
Practical applications of multi-agent systems in electric power systems
The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur
Bayesian Software Health Management for Aircraft Guidance, Navigation, and Control
Modern aircraft, both piloted fly-by-wire commercial aircraft as well as UAVs, more and more depend on highly complex safety critical software systems with many sensors and computer-controlled actuators. Despite careful design and V&V of the software, severe incidents have happened due to malfunctioning software. In this paper, we discuss the use of Bayesian networks (BNs) to monitor the health of the on-board software and sensor system, and to perform advanced on-board diagnostic reasoning. We will focus on the approach to develop reliable and robust health models for the combined software and sensor systems
Monitoring and analysis of data from complex systems
Some of the methods, systems, and prototypes that have been tested for monitoring and analyzing the data from several spacecraft and vehicles at the Marshall Space Flight Center are introduced. For the Huntsville Operations Support Center (HOSC) infrastructure, the Marshall Integrated Support System (MISS) provides a migration path to the state-of-the-art workstation environment. Its modular design makes it possible to implement the system in stages on multiple platforms without the need for all components to be in place at once. The MISS provides a flexible, user-friendly environment for monitoring and controlling orbital payloads. In addition, new capabilities and technology may be incorporated into MISS with greater ease. The use of information systems technology in advanced prototype phases, as adjuncts to mainline activities, is used to evaluate new computational techniques for monitoring and analysis of complex systems. Much of the software described (specially, HSTORESIS (Hubble Space Telescope Operational Readiness Expert Safemode Investigation System), DRS (Device Reasoning Shell), DART (Design Alternatives Rational Tool), elements of the DRA (Document Retrieval Assistant), and software for the PPS (Peripheral Processing System) and the HSPP (High-Speed Peripheral Processor)) is available with supporting documentation, and may be applicable to other system monitoring and analysis applications
Fault Detection and Diagnosis in Air Conditioners and Refrigerators
A fault detection and diagnosis (FDD) method was used to detect and diagnose
faults on both a refrigerator and an air conditioner during normal cycling operation. The
objective of the method is to identify a set of sensors that can detect faults reliably before
they severely hinder system performance. Unlike other methods, this one depends on the
accuracy of a number of small, on-line linear models, each of which is valid over a
limited range of operating conditions.
To detect N faults, N sensors are needed. Using M>N sensors can further reduce
the risk of false positives. For both the refrigerator and air conditioner systems, about
1000 combinations of candidate sensor locations were examined. Through inspection of
matrix condition numbers and each sensor's contribution to fault detection calculation, the
highest quality sets of sensors were identified. The issue of detecting simultaneous
multiple faults was also addressed, with varying success.
Fault detection was verified using both model simulations and experimental data.
The results were similar, although in practice only one of the two would probably be
used. Both load-type faults (such as door gasket leaks) and system faults were simulated
on the refrigerator. It was found that system faults were generally more easily detectable
than load faults.
Refrigerator experiments were performed on a typical household refrigerator
because it was readily available in a laboratory, but the results of this project may be
more immediately useful on larger commercial, industrial or transport refrigeration
systems. Air conditioner experiments were performed on a 3-ton split system. Again, the
economic benefits of this type of fault detection scheme may also be more feasible for
larger field-assembled systems.Air Conditioning and Refrigeration Project 8
Flight deck engine advisor
The focus of this project is on alerting pilots to impending events in such a way as to provide the additional time required for the crew to make critical decisions concerning non-normal operations. The project addresses pilots' need for support in diagnosis and trend monitoring of faults as they affect decisions that must be made within the context of the current flight. Monitoring and diagnostic modules developed under the NASA Faultfinder program were restructured and enhanced using input data from an engine model and real engine fault data. Fault scenarios were prepared to support knowledge base development activities on the MONITAUR and DRAPhyS modules of Faultfinder. An analysis of the information requirements for fault management was included in each scenario. A conceptual framework was developed for systematic evaluation of the impact of context variables on pilot action alternatives as a function of event/fault combinations
An Adaptive Resonance Theory Neural Network (ART NN)-based fault diagnosis system: A Case Study of gas turbine system in Resak Development Platform
The project introduces a case study of a real gas turbine system in Resak Development
Platform. There are two main objectives of this project. The first objective is aimed to
achieve an online fault diagnosis model using Adaptive Resonance Theorem (ART) as a
considered option to avoid potential faults happen during plant system and process. The
second objective is focused on a solution to improve the maintenance plan for the gas
turbine system to be more economical yet still maintaining its safety level
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