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检索条件"任意字段=Conference on System Diagnosis and Prognosis: Security and Condition Monitoring Issues III"

共 8 条记录，以下是1-10 订阅

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Proceedings of SPIE: system diagnosis and prognosis: security and condition monitoring issues iii

Proceedings of SPIE: System Diagnosis and Prognosis: Securit...

引用

PROCEEDINGS OF SPIE SPIE - The International Society for Optical Engineering: system diagnosis and prognosis: security and condition monitoring issues iii

The proceedings contains 15 papers from the conference on proceedings of SPIE: system diagnosis and prognosis: security and condition monitoring issues iii. The topics discussed include: prognostics for advanced compressor health monitoring;intelligent model-based diagnostics for vehicle health management;multiphase reliability analysis of complex systems;a fuzzy Petri-net-based mode identification algorithm for fault diagnosis of complex systems and model-supported diagnosis for integrated vehicle health mangagement of space systems.

关键词： Compressors

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Human detection and motion analysis at security points

Human detection and motion analysis at security points

引用

conference on system diagnosis and prognosis: security and condition monitoring issues iii

作者： Ozer, B Lv, T Wolf, W Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)0819449679

This paper presents a real-time video surveillance system for the recognition of specific human activities. Specifically, the proposed automatic motion analysis is used as an on-line alarm system to detect abnormal situations in a campus environment. A smart multi-camera system developed at Princeton University is extended for use in smart environments in which the camera detects the presence of multiple persons as well as their gestures and their interaction in real-time.

关键词： multiple person detection real-time activity recognition video surveillance multi-camera systems abnormal situation detection

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Model-supported diagnosis for integrated vehicle health management of space systems

Model-supported diagnosis for integrated vehicle health mana...

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conference on system diagnosis and prognosis: security and condition monitoring issues iii

作者： Dannenmann, P Busch, W German Res Ctr Artificial Intelligence D-67608 Kaiserslautern Germany

ISBN: (纸本)0819449679

In this paper we present a new architecture for integrating system health monitoring tasks into the development- and life cycle of space systems. On the basis of model-supported diagnosis technology the presented method uses information for diagnosis purposes that is already gathered during the development of a technical system. This information is extracted from simulation models used for design-studies and what-if-analyses during the design- and development phase. For building up these simulation models easily, we developed a library of generic models of spacecraft components. These models cover the components' nominal and off-non-final behavior as it is specified in the component FMECAs. By combining and parametrizing the components a system model is built up. Since due to the limited resources on board of a spacecraft we can not use the model directly for model-based diagnosis, we use a model-supported approach: By systematically simulating possible component faults within the system's operational modes, we retrieve a set of measurement data that serve as symptoms to the failure modes. By classifying these data we get a knowledge-base for a symptom-based on-board diagnosis system. In order to cope with the uncertainty in the measurement data, this diagnosis system has been realized as a fuzzy system that on the basis of the given knowledge-base computes the most probable diagnoses from the given symptoms. The described system has been implemented within Astrium's Columbus Ground system (CGS) and has been evaluated on several aerospace systems ranging from an unmanned aerial robot on the basis of an airship to the Propulsion and Reboost Subsystem of the Automated Transfer Vehicle (ATV), a supply spacecraft for the International Space Station.

关键词： system health monitoring model-supported diagnosis simulation component model library

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Sensor validation and fusion for gas turbine vibration monitoring

Sensor validation and fusion for gas turbine vibration monit...

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conference on system diagnosis and prognosis: security and condition monitoring issues iii

作者： Yan, WZ Goebel, K GE Global Res Ctr Niskayuna NY 12309 USA

ISBN: (纸本)0819449679

Vibration monitoring is an important practice throughout regular operation of gas turbine power systems and, even more so, during characterization tests. Vibration monitoring relies on accurate and reliable sensor readings. To obtain accurate readings, sensors are placed such that the signal is maximized. In the case of characterization tests, strain gauges are placed at the location of vibration modes on blades inside the gas turbine. Due to the prevailing harsh environment, these sensors have a limited life and decaying accuracy, both of which impair vibration assessment. At the same time bandwidth limitations may restrict data transmission, which in turn limits the number of sensors that can be used for assessment. Knowing the sensor status (normal or faulty), and more importantly, knowing the true vibration level of the system all the time is essential for successful gas turbine vibration monitoring. This paper investigates a dynamic sensor validation and system health reasoning scheme that addresses the issues outlined above by considering only the information required to reliably assess system health status. In particular, if abnormal system health is suspected or if the primary sensor is determined to be faulted, information from available "sibling" sensors is dynamically integrated. A confidence expresses the complex interactions of sensor health and system health, their reliabilities, conflicting information, and what the health assessment is. Effectiveness of the scheme in achieving accurate and reliable vibration evaluation is then demonstrated using a combination of simulated data and a small sample of a real-world application data where the vibration of compressor blades during a real time characterization test of a new gas turbine power system is monitored.

关键词： sensor validation diagnostics information fusion data fusion simulation monitoring vibration

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Automated situational awareness sensing for homeland defense

Automated situational awareness sensing for homeland defense

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conference on system diagnosis and prognosis: security and condition monitoring issues iii

作者： Reichard, KM Crow, EC Swanson, DC Penn State Univ Appl Res Lab State Coll PA 16804 USA

ISBN: (纸本)0819449679

Advances in information technology enable a capability to gather large amounts of raw data from all parts of our society to a degree of efficiency that can actually encumber surveillance missions. Today one can create automated networks of sensors to gather enormous volumes of data, but we do not have the human capacity to sort through all this raw data. Similar problems exist in the area of machinery condition or health monitoring - another surveillance problem. By automating data collection, processing, fusion and interpretation. one can bring the most relevant and timely information to human analysts, planners, and responders. Key technologies that enable this transformation from data to knowledge are distributed hardware and software architectures, intelligent sensors, data fusion and reasoning algorithms, and open system architectures for information exchange. Distributed hardware and software structures partition complex systems into a collection of interconnected subsystems. Low-level, wide-bandwidth sensor data can be confined at the subsystems level and only higher-level information needs to be shared between monitoring modules for different subsystems or at higher levels. Intelligent sensors enable this conversion of data to information by processing massive amounts of data at the subsystem and higher levels to extract contextually relevant information. Intelligent sensors may consist of traditional transducer elements, electronics, and processing capability, or may exist as virtual processing nodes resident on a data network. The transformation from raw sensor data to useful information requires the application of subsystem-specific signal processing and feature extraction algorithms, data fusion, and classification algorithms to combine data and features from commensurate and non-commensurate sensors or information sources. Emerging standards in open system architectures for condition based maintenance will ease the integration of monitoring system compo

关键词： situational awareness automation machinery condition monitoring sensing networking

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Prognostics for advanced compressor health monitoring

Prognostics for advanced compressor health monitoring

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conference on system diagnosis and prognosis: security and condition monitoring issues iii

作者： Krok, M Goebel, K GE Global Res Niskayuna NY 12309 USA

ISBN: (纸本)0819449679

Axial flow compressors are subjected to demands for ever-increasing levels of pressure ratio at a compression efficiency that augments the overall cycle efficiency. However, unstable flow may develop in the compressor, which can lead to a stall or surge and subsequently to gas turbine failure resulting in significant downtime and cost to repair. To protect against these potential aerodynamic instabilities, compressors are typically operated with a stall margin. This means operating the compressor at less than peak pressure rise which results in a reduction in operating efficiency and performance. Therefore, it is desirable to have a reliable method to determine the state of a compressor by detecting the onset of a damaging event prior to its occurrence. In this paper, we propose a health monitoring scheme that gathers and combines the results of different diagnostic tools to maximize the advantages of each one while at the same time minimizing their disadvantages. This fusion scheme produces results that are better than the best result by any one tool used. In part this is achieved because redundant information is available that when combined correctly improves the estimate of the better tool and compensates for the shortcomings of the less capable tool. We discuss the usage of diagnostic information fusion for a compressor event coupled with proactive control techniques to support improved compressor performance while at the same time avoid the increased damage risk due to stall margin reduction. Discretized time to failure windows provide event prediction in a prognostic sense.

关键词： sensor validation prognostics diagnostics information fusion data fusion simulation surge stall

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Detection of intrusion across multiple sensors

Detection of intrusion across multiple sensors

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conference on system diagnosis and prognosis: security and condition monitoring issues iii

作者： Long, W Doyle, J Burke, G Szolovits, P MIT Comp Sci Lab Cambridge MA 02139 USA

ISBN: (纸本)0819449679

We have been developing an architecture for reasoning with multiple sensors distributed on a computer network, linking them with analysis modules, and reasoning with the results to combine evidence of possible intrusion for display to the user. The architecture, called MAITA, consists of monitors distributed across machines and linked together under control of the user and supported by a "monitor of monitors" that manages the interaction among the monitors. This architecture enables the system to reason about evidence from multiple sensors. For example, a monitor can track FTP logs to detect password scans followed by successful uploads of data from foreign sites. At the same time it can monitor disk use and detect significant trends. A monitor can then combine the evidence in the sequence in which it occurs and present evidence to the user that someone has successfully gained write access to the FTP site and is occupying significant disk space. This paper discusses the architecture enabling the creation, linking, and support of the monitors. The monitors may be running on the same or different machines and so appropriate communication links must be supported as well as regular status checks to ensure that monitors are still running. We will also discuss the construction of monitors for sensing the data, abstracting and characterizing data, synchronizing data from different sources, detecting patterns, and displaying the results.

关键词： intrusion detection architecture sensors distributed monitoring

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Wondering what to blame? Turn PV performance assessments into maintenance action items through the deployment of learning algorithms embedded in a Raspberry Pi device 43

Wondering what to blame? Turn PV performance assessments int...

引用

43rd IEEE Photovoltaic Specialists conference (PVSC)

作者： Jones, C. Birk Martinez-Ramon, Manel Carmignani, Craig Stein, Joshua S. King, Bruce H. Sandia Natl Labs Solar PV & Grid Integrat POB 5800 Albuquerque NM 87185 USA Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Univ Carlos III Madrid Madrid Spain

ISBN: (纸本)9781509027248

monitoring of photovoltaic (PV) systems can maintain efficient operations. However, extensive monitoring of large quantities of data can be a cumbersome process. The present work introduces a simple, inexpensive, yet effective data monitoring strategy for detecting faults and determining lost revenues automatically. This was achieved through the deployment of Raspberry Pi (RPI) device at a PV system's combiner box. The RPI was programmed to collect PV data through Modbus communications, and store the data locally in a MySQL database. Then, using a Gaussian Process Regression algorithm the RPI device was able to accurately estimate string level current, voltage, and power values. The device could also detect system faults using a Support Vector Novelty Detection algorithm. Finally, the RPI was programmed to output the potential lost revenue caused by the abnormal condition. The system analytics information was then displayed on a user interface. The interface could be accessed by operations personal to direct maintenance activity so that critical issues can be solved quickly.

关键词： Gaussian Process support vector machine Raspberry Pi modbus photovoltaics data collection fault detection

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