Near Real-time Autonomous Quality Control for Streaming Environmental Sensor Data

Scully-Allison, Connor; Le, Vinh D.; Fritzinger, Eric; Strachan, Scotty; Harris, Frederick C.; Dascalu, Sergiu M.

doi:10.1016/j.procs.2018.08.139

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Hereby, QA can be considered a proactive or preventive process to avoid problems and QC as a process to identify and flag suspect data after they have been generated. (Campbell et al, 2013;Scully-Allison et al, 2018) The QC procedures can be applied at various stages of data flow from sensors to the end user and can be carried out by numerous methods. Observations can be flagged by several methods and various symbols and names can be used to indicate the quality control level.…”

Section: Quality Flaggingmentioning

confidence: 99%

“…(Campbell et al, 2013;Vejen et al, 2002). Hence, several QC methods for automatic tests have been reported (Vejen et al 2002;Scully-Allison et al 2018;Geuder et al, 2015;Lewis et al, 2018) In addition to the simple and traditional QC procedures, methods utilizing machine-learning have also been developed. These methods represent a datadriven approach to QC, wherein statistical models or classifiers are trained using empirical data collected from sensors.…”

Section: Quality Flaggingmentioning

confidence: 99%

See 1 more Smart Citation

Adaptation framework for an industrial digital twin

Koistinen¹,

Ohenoja²,

Tomperi³

et al. 2021

Linköping Electronic Conference Proceedings

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Digital twins for performance-oriented applications in industrial environments require systematic model maintenance. Model adaptation requires efficient optimization tools and continuous evaluation of measurement quality. The adaptation and model performance evaluation are based on the modeling error, making the adaptation prone also to the measurement errors. In this paper, a framework for combining model adaptation and measurement quality assurance are discussed. Two examples with simulated industrialscale biopharmaceutical penicillin fermentation are presented to illustrate the usability of the framework.

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Section: Quality Flaggingmentioning

confidence: 99%

Section: Quality Flaggingmentioning

confidence: 99%

Adaptation framework for an industrial digital twin

Koistinen¹,

Ohenoja²,

Tomperi³

et al. 2021

Linköping Electronic Conference Proceedings

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show abstract

“…Looking toward a future in which the management of environmental systems is driven by vast quantities of real-time data, it is no longer feasible for the QAQC process to be entirely manual. ,, A number of approaches have been proposed to detect environmental sensor data faults. These approaches largely fall into three categories, (1) heuristic rule-based tests, ,− (2) ML and purely statistical methods, ,, and (3) multisensor comparison. − Some studies have applied a combination of these. − These existing methods are often unsuitable for use on large-scale, low-resource sensor networks, however, as they are either expensive, labor-intensive to scale, or anticipate a minimum signal-to-noise ratio to which real-world sensors may not adhere to. In recent years, most QAQC approaches have been built around data that are reliable enough to be cleaned to begin with.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Assisted, Process-Based Quality Control for Detecting Compromised Environmental Sensors

Schmidt,

Kerkez

2023

Environ. Sci. Technol.

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Machine learning (ML) techniques promise to revolutionize environmental research and management, but collecting the necessary volumes of high-quality data remains challenging. Environmental sensors are often deployed under harsh conditions, requiring labor-intensive quality assurance and control (QAQC) processes. The need for manual QAQC is a major impediment to the scalability of these sensor networks. Existing techniques for automated QAQC make strong assumptions about noise profiles in the data they filter that do not necessarily hold for broadly deployed environmental sensors, however. Toward the goal of increasing the volume of high-quality environmental data, we introduce an ML-assisted QAQC methodology that is robust to low signal-to-noise ratio data. Our approach embeds sensor measurements into a dynamical feature space and trains a binary classification algorithm (Support Vector Machine) to detect deviation from expected process dynamics, indicating whether a sensor has become compromised and requires maintenance. This strategy enables the automated detection of a wide variety of nonphysical signals. We apply the methodology to three novel data sets produced by 136 low-cost environmental sensors (stream level, drinking water pH, and drinking water electroconductivity), deployed by our group across 250,000 km 2 in Michigan, USA. The proposed methodology achieved accuracy scores of up to 0.97 and consistently outperformed state-of-the-art anomaly detection techniques.

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“…Most of them rely on visual screening of data, and therefore personal inspection, and on manual elimination of erroneous values based on empirical knowledge and investigator experiences. Several advanced tools such as GCE (Scully-Allison et al, 2018), CoTeDe (Castelao, 2016), AutoQC (IQuOD) and comprehensive user manuals such as QARTOD (Willis et al, 2016), WMO-AWS (Zahumensky, 2004) have been developed with precise rules to overcome this subjectivity.…”

mentioning

confidence: 99%

A data-driven persistence test for robust (probabilistic) quality control of measured environmental time series: constant value episodes

Kaffashzadeh

2023

Preprint

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Abstract. Robust quality control is a prerequisite and an essential component in any data application. That is especially important for time series of environmental observations such as air quality due to their dynamic and irreversible nature. One of the common issues in these data is constant value episodes (CVEs), where a set of consecutive data values remains constant over a given period. Although CVEs are often considered as an indicator of sensor failure or other measurement errors and removed during quality control procedures, there are situations when CVEs reflect natural environmental phenomena, and they should not be removed from the data or analysis. Assessing whether the CVEs are erroneous data or valid observations is a challenge. As there are no formal procedures established for this, their classification is based on subjective judgement and therefore uncertain and irreproducible. This paper presents a novel test procedure, i.e., constant value test, to estimate the probability of CVEs being valid data. The theoretical foundation of this test is based on statistical characteristics and probability theory and takes into account the numerical precision of the data values. The test is a datadriven (parametric) approach, which makes it usable for time series analysis in different environmental research domains, as long as serial dependency is given and the data distribution is not too different from Gaussian. The robustness of the test was demonstrated with sensitivity studies using synthetic data with different distributions. Example applications to measured air temperature and ozone mixing ratio data confirm the versatility of the test.

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Near Real-time Autonomous Quality Control for Streaming Environmental Sensor Data

Cited by 7 publications

References 5 publications

Adaptation framework for an industrial digital twin

Adaptation framework for an industrial digital twin

Machine Learning-Assisted, Process-Based Quality Control for Detecting Compromised Environmental Sensors

A data-driven persistence test for robust (probabilistic) quality control of measured environmental time series: constant value episodes

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