On-line sensor validation of single sensors using artificial neural networks

Himmelblau, D. M.; Bhalodia, Mohan

doi:10.1109/acc.1995.529354

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Sensor validation methods using temporal redundancy depend on the analysis of sensor values over time, using filtering techniques (Massoumnia, 1986) and Bayesian approaches (Dragoni, Giorgini & Pant, 1998) for example. Pattern recognition techniques (Griebenow, Hansen & Sudduth, 1995) and neural nets (Himmelblau & Bhalodia, 1995) have also been used for sensor validation. Overall, such advances have made it possible to consider the development of ''smart sensors'' that will be self-calibrating and self-diagnosing (Doyle, Garrison, Johnson & Smith, 1998).…”

Section: Instrumentation and Control Engineering Methods To Handle Sementioning

confidence: 99%

Work domain analysis and sensors I: principles and simple example

Reising

Sanderson

2002

International Journal of Human-Computer Studies

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In this paper we establish a foundation for understanding the instrumentation needs of complex dynamic systems if ecological interface design (EID)-based interfaces are to be robust in the face of instrumentation failures. EID-based interfaces often include configural displays which reveal the higher-order properties of complex systems. However, concerns have been expressed that such displays might be misleading when instrumentation is unreliable or unavailable. Rasmussen's abstraction hierarchy (AH) formalism can be extended to include representations of sensors near the functions or properties about which they provide information, resulting in what we call a ''sensorannotated abstraction hierarchy''. Sensor-annotated AHs help the analyst determine the impact of different instrumentation engineering policies on higher-order system information by showing how the data provided from individual sensors propagates within and across levels of abstraction in the AH. The use of sensor-annotated AHs with a configural display is illustrated with a simple water reservoir example. We argue that if EID is to be effectively employed in the design of interfaces for complex systems, then the information needs of the human operator need to be considered at the earliest stages of system development while instrumentation requirements are being formulated. In this way, Rasmussen's AH promotes a formative approach to instrumentation engineering.# 2002 Elsevier Science Ltd. All rights reserved.KEYWORDS: work domain analysis; sensors; instrumentation; abstraction hierarchy; ecological interface design.''What can be sensed forms a fundamental limiting feature of displays. This limiting feature is not always given the emphasis it deserves. [. . .] A measuring instrument which indicated directly and accurately its position relative to the earth's surface would render obsolete the entire science of navigation as we know it. This illustrates clearly the limits imposed on navigation systems by lack of a sensing instrument. The whole system of celestial and radio fixes, the use of compass information, inertial and other ''dead reckoning'' systems, are all substitutes for what we would like to sense directly and display but do not yet have the sensing means for. The initial step in considering the design of the displays for a particular manual control system is to analyze the information the operator would really like to have and to consider the sensing instruments available to obtain it for him. Too often this analysis is not carried out, and it is assumed that the operator requires information that, for instance, has been displayed on similar systems in the past . . . '' Kelley (1968, p. 90-91).

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Section: Instrumentation and Control Engineering Methods To Handle Sementioning

confidence: 99%

Work domain analysis and sensors I: principles and simple example

Reising

Sanderson

2002

International Journal of Human-Computer Studies

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“…Measurement aberration detection denotes methods and algorithms that attempt to identify faults locally at the sensor level. Himmelblau and Bhalodia propose sensor validation using statistical tests and signal modeling with artificial neural networks. Time-series models have also been used. , Luo et al used multiscale (wavelet) filters to retain the mid-frequency noise and applied various statistical tests to identify sensor degradation.…”

Section: Introductionmentioning

confidence: 99%

New in Situ Sensor Modeling Approach to Measurement Validation

Srinivasagupta

Joseph

Morley

2003

Ind. Eng. Chem. Res.

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Measurement error due to sensor degradation (fouling, miscalibration, etc.) is more difficult to identify compared to catastrophic sensor failure. Passive methods previously proposed for sensorlevel monitoring are based on power spectrum or multiscale analysis of sensor data. These methods have limitations caused by not accounting for various noise sources and assumptions about sensor noise characteristics, thus resulting in false and missed alarms. In this paper, an online sensor fault detection scheme based on the identification of sensor response characteristics is proposed and evaluated. We develop both robust passive and active in situ techniques to identify sensor response characteristics that relate directly to its health. Using the identified sensor model, various kinds of sensor faults are quantified and mapped into the model parameters. A dynamic model-based estimator is proposed for data reconciliation. These ideas were experimentally validated using thermocouples, flowmeters, and resistance thermometric devices on laboratory-scale processes. The proposed approach was seen to accurately quantify the sensor model parameters and aid in measurement reconstruction.

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A self-diagnosing distributed monitoring system for nuclear power plants

Dragoni

Giorgini

Panti

1998

Lecture Notes in Computer Science

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Abstract. Sensor data fusion and interpretation, sensor failure detection, isolation and identification are extremely important activities for the safety of a nucletU' power plant. In pm·ticular, they become critical in cases of conflicts among the data. If the monitored system's description model is correct and its component~ work properly, then incompatibilities among data may only be attributed to temporary deterioration or permanent breakage of one or more sensors. This paper introduces and discusses the conception of a distributed monitoring system able to attach each sensor with a statistically-evaluated relative degree of reliability, which is especially useful for devices situated in dangerous zones or areas, difficult to reach inside huge and complex power plants.

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On-line sensor validation of single sensors using artificial neural networks

Cited by 6 publications

References 4 publications

Work domain analysis and sensors I: principles and simple example

Work domain analysis and sensors I: principles and simple example

New in Situ Sensor Modeling Approach to Measurement Validation

A self-diagnosing distributed monitoring system for nuclear power plants

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