Weather Sensor Fault Detection with Time-Dependent Recursive Thresholds

Hasu, Vesa; Koivo, H.N.; Poutiainen, Jani

doi:10.1109/icicic.2006.545

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…where a t and b t denotes by the predicted dispersion value of slope and y-intercept at each discrete time t. Sensor data is time-dependent [24], the slope and y-intercept of the baseline in each linearly representable range are constantly changing, which contain potential information related to the sensors. The temperature change is also an important factor affecting baseline drift.…”

Section: Algorithm For Baseline Estimation and Drift Compensationmentioning

confidence: 99%

A two-stage method for real-time baseline drift compensation in gas sensors

Zhang

Wang

Pan³

et al. 2022

Meas. Sci. Technol.

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Baseline drift caused by slowly changing environment and other instability factors affects significantly the performance of gas sensors, resulting in reduced accuracy of gas classification and quantification of the electronic nose. In this work, a two-stage method is proposed for real-time sensor baseline drift compensation based on estimation theory and piecewise linear approximation. In the first stage, the linear information from the baseline before exposure is extracted for prediction. The second stage continuously predicts changing linear parameters during exposure by combining temperature change information and time series information, and then the baseline drift is compensated by subtracting the predicted baseline from the real sensor response. The proposed method is compared to three efficient algorithms and the experiments are conducted towards two simulated datasets and two surface acoustic wave sensor datasets. The experimental results prove the effectiveness of the proposed algorithm. Moreover, the proposed method can recover the true response signal under different ambient temperatures in real-time, which can guide the future design of low-power and low-cost rapid detection systems.

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Section: Algorithm For Baseline Estimation and Drift Compensationmentioning

confidence: 99%

A two-stage method for real-time baseline drift compensation in gas sensors

Zhang

Wang

Pan³

et al. 2022

Meas. Sci. Technol.

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“…The Helsinki Testbed data have also been used in developing new data quality-control and faultdetection methods and maintenance strategies for dense observation networks (Hasu et al 2006a,b;Hasu and Koivo 2007).…”

Section: Cooperation Through Research Projectsmentioning

confidence: 99%

The Helsinki Testbed: A Mesoscale Measurement, Research, and Service Platform

Koskinen

Poutiainen

Schultz

et al. 2011

Bulletin of the American Meteorological Society

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The Finnish Meteorological Institute and Vaisala have established a mesoscale weather observational network in southern Finland. The Helsinki Testbed is an open research and quasi-operational program designed to provide new information on observing systems and strategies, mesoscale weather phenomena, urban and regional modeling, and end-user applications in a high-latitude (~60°N) coastal environment. The Helsinki Testbed and related programs feature several components: observing system design and implementation, small-scale data assimilation, nowcasting and short-range numerical weather prediction, public service, and commercial development of applications. Specifically, the observing instrumentation focuses on meteorological observations of meso-gamma-scale phenomena that are often too small to be detected adequately by traditional observing networks. In particular, more than 40 telecommunication masts (40 that are 120 m high and one that is 300 m high) are instrumented at multiple heights. Other instrumentation includes one operational radio sounding (and occasional supplemental ones), ceilometers, aerosol-particle and trace-gas instrumentation on an urban flux-measurement tower, a wind profiler, and four Doppler weather radars, three of which have dual-polarimetric capability. The Helsinki Testbed supports the development and testing of new observational instruments, systems, and methods during coordinated field experiments, such as the NASA Global Precipitation Measurement (GPM). Currently, the Helsinki Testbed Web site typically receives more than 450,000 weekly visits, and more than 600 users have registered to use historical data records. This article discusses the three different phases of development and associated activities of the Helsinki Testbed from network development and observational campaigns, development of the local analysis and prediction system, and testing of applications for commercial services. Finally, the Helsinki Testbed is evaluated based on previously published criteria, indicating both successes and shortcomings of this approach.

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“…The fault detection residuals mentioned in Subsection IV.A are from the algorithm introduced in [8] and [9]. Neighbors used in the computation of estimability are listed in Table I.…”

Section: B Numerical Examplementioning

confidence: 99%

“…r is the difference between the measurement and its filtered estimate. Residuals are obtained from the fault diagnosis algorithm [9] for determination of faulty measurements. The idea is to punish for large residual values as well as too large residual variation.…”

Section: A Mathematical Definitionsmentioning

confidence: 99%

Maintenance variables for spatially distributed measurement networks

Hasu¹,

Koivo²

2007

2007 IEEE International Conference on Systems, Man and Cybernetics

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Maintenance of widely distributed sensor networks becomes a big issue in the future with more cheaper sensor nodes. This paper describes and discusses a method including five maintenance variables for characterization of the state of spatially distributed measurement network. The variables are aimed to describe the reliability of the network for end user and to help making automated maintenance decisions. The descriptive concepts are called availability, accuracy, reliability, estimability and influence. All of these are described in numerical form in a case example concerning a weather station network.

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Weather Sensor Fault Detection with Time-Dependent Recursive Thresholds

Cited by 4 publications

References 8 publications

A two-stage method for real-time baseline drift compensation in gas sensors

A two-stage method for real-time baseline drift compensation in gas sensors

The Helsinki Testbed: A Mesoscale Measurement, Research, and Service Platform

Maintenance variables for spatially distributed measurement networks

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