Accounting for sensor calibration, concentration heterogeneity, measurement and sampling uncertainties in monitoring urban drainage systems

Bertrand-Krajewski, Jean-Luc; Bardin, Jean-Pascal; Mourad, M.; Béranger, Yvan

doi:10.2166/wst.2003.0094

Cited by 45 publications

(28 citation statements)

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“…To determine whether a sensor node is malfunctioning, validation methods are applied to sensor data, aiming to find devious sensor readings from normal ones. There is no such an ideal validation method, because they are very dependent on sensor measurement conditions and the overall environment context (Freitas et al, 2010), (Branisavljević et al, 2011), (Bertrand-Krajewski et al, 2003), (Vasconcelos et al, 2012), and (Liu et al, 2013). Usually, several methods are applied successively to the sensor data, because each method is suitable to detect particular types of faulty data.…”

Section: Sensor Data Validationmentioning

confidence: 99%

Wireless Sensor Network Simulation for Fault Detection in Industrial Processes

Pinto

Rossetti

Gonçalves

2016

Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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Abstract:Sensor data is extremely important to monitor machines at the shop-floor level and its environmental surrounding conditions for condition-based monitoring, machine diagnosis and process adaptation to new requirements. Based on the described scope, self-diagnostics and self-organizing capabilities are core functionalities of any Industrial Wireless Sensor Network (IWSN). In the present work, a simulated case study was developed with the main intent of validating techniques implemented for sensor data diagnosis of error detection and equipment failure. The scenarios explored try to mimic some common situations of a manufacturing environment when dealing with WSNs, where a piece of sensor equipment suddenly stops working or an unpredictable change in the environment leads to faulty data readings. This paper introduces Castalia and describes how it was used to simulate a direct application of an Optical Metrology System on an industrial Resistance Spot Welding process, which is composed of a camera and several luminosity sensors. More specifically, a sensor data validation module was proposed, implemented and used to extend Castalia functionalities.

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Section: Sensor Data Validationmentioning

confidence: 99%

Wireless Sensor Network Simulation for Fault Detection in Industrial Processes

Pinto

Rossetti

Gonçalves

2016

Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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“…However, corresponding investment in sampling equipment are negligible by comparison. The importance of sampling in terms of errors has been demonstrated in numerous domains, and also in environmental areas (Christakos and Olea, 1992;Littlewood, 1995;Isidoro et al 2003;Bertrand-Krajewski et al 2003;Johnes, 2007;Ort and Gujer 2006). For example, large uncertainties (30%-100%) in stormwater data result from the different sampling and analytical steps (sampling of water, sample conservation, reconstitution of a flow-weighted composite sample and chemical laboratory analyses) (Rossi 1998).…”

Section: Introductionmentioning

confidence: 99%

Samplinghelper a web-based tool to assess the reliability of sampling strategies in sewers and receiving waters

Rossi

Rumley

Ort

et al. 2011

Water Science and Technology

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RÉSUMÉL'échantillonnage est une étape clé dans l'analyse de composés physico-chimiques. Cette étape est particulièrement importante dans le domaine environnemental, par exemple pour les eaux usées, les eaux de ruissellement ou les milieux récepteurs dans lesquels les débits et les concentrations varient fortement au cours du temps. Contrairement aux efforts réalisés spécifiquement en chimie analytique, les développements dans le domaine de l'échantillonnage restent rares. Cette étape peut néanmoins générer des erreurs dans un ordre de grandeur bien supérieur à celui analytique. Dans cet article, nous proposons un outil internet, basé sur une théorie de l'échantillonnage pour identifier et quantifier les erreurs dans les différents processus de prise d'échantillons. Cette théorie générale de l'échantillonnage, déjà appliquée à différents domaines, permet notamment de donner des réponses à des questions liées au nombre d'échantillons nécessaires, à leur volume, à leur représentativité, etc. L'utilisation d'internet pour héberger cette application est destinée à faciliter l'utilisation des outils théoriques et de sensibiliser les professionnels aux incertitudes liées à l'échantillonnage. Un exemple est présenté, qui met en évidence l'importance de l'étape d'échantillonnage dans la qualité des résultats d'analyse. MOTS CLÉSEchantillonnage, représentativité, erreurs, incertitudes, hétérogénéité ABSTRACTSampling is a key step in the analysis of chemical compounds. It is particularly important in the environmental field, for example for wastewater effluents, wet-weather discharges or streams in which the flows and concentrations vary greatly over time. In contrast to the improvements that have occurred in analytical measurement, developments in the field of sampling are less active. However, sampling errors may exceed by an order of magnitude those related to analytical processes. We proposed an Internet-based application based on a sampling theory to identify and quantify the errors in the process of taking samples. This general theory of sampling, already applied to different areas, helps to answer questions related to the number of samples, their volume, their representativeness, etc. The use of the internet to host this application facilitates use of theoretical tools and raise awareness of the uncertainties related to sampling. An example is presented, which highlights the importance of the sampling step in the quality of analytical results.

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“…Other well-known limitations to this approach are sampling errors and errors due to sample conservation, transport and preparation (see e.g. Bertrand- Krajewski et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Sewer modelling based on highly distributed calibration data sets and multi-objective auto-calibration schemes

Muschalla

Schneider

Schröter

et al. 2008

Water Science and Technology

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Pollutant load modelling for sewer systems is state-of-the-art, especially for the estimation of discharged pollutant loads and development of sewer management strategies. However, conventionally obtained calibration data sets are often not exhaustive and have significant drawbacks. In the Graz West catchment area (Graz, Austria), continuous high-resolution long-term online measurements for discharge and pollutant concentration have been carried out since 2002. In this paper, the application of single- and multi-objective auto-calibration schemes based on evolution strategies for a deterministic hydrological pollutant load model will be discussed. Three approaches for pollutant load modelling are examined and compared: using a constant storm weather concentration and two build-up wash-off approaches with basic respectively extended wash-off equations. It is shown that the applied auto-calibration method leads to very satisfying results for both the calibration and the validation data set, and also for the dry and the storm weather runoff. However, until now, convective storms have not been convincingly represented. The build-up wash-off approach using the basic wash-off equation shows the best correlations between measured data and simulation results. As one of the chosen objectives for the multi-objective optimisation reacted highly sensitively to measurement errors, additional improvements can be expected after refining the criteria used in this algorithm.

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Accounting for sensor calibration, concentration heterogeneity, measurement and sampling uncertainties in monitoring urban drainage systems

Cited by 45 publications

References 0 publications

Wireless Sensor Network Simulation for Fault Detection in Industrial Processes

Wireless Sensor Network Simulation for Fault Detection in Industrial Processes

Samplinghelper a web-based tool to assess the reliability of sampling strategies in sewers and receiving waters

Sewer modelling based on highly distributed calibration data sets and multi-objective auto-calibration schemes

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