Enhanced data validation strategy of air quality monitoring network

Harkat, Mohamed-Faouzi; Mansouri, Majdi; Nounou, Mohamed; Nounou, Hazem

doi:10.1016/j.envres.2017.09.023

Cited by 39 publications

(16 citation statements)

References 31 publications

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“…e AIRLOR (air quality monitoring network) is used in this part, operating in Lorraine, France. AIRLOR is a network which contains twenty stations spread out over several sites: urban, periurban, and rural [4].…”

Section: Air Quality Descriptionmentioning

confidence: 99%

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Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

Saïd

Taouali

2021

Mathematical Problems in Engineering

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We suggest in this article a dynamic reduced algorithm in order to enhance the monitoring abilities of nonlinear processes. Dynamic fault detection using data-driven methods is among the key technologies, which shows its ability to improve the performance of dynamic systems. Among the data-driven techniques, we find the kernel partial least squares (KPLS) which is presented as an interesting method for fault detection and monitoring in industrial systems. The dynamic reduced KPLS method is proposed for the fault detection procedure in order to use the advantages of the reduced KPLS models in online mode. Furthermore, the suggested method is developed to monitor the time-varying dynamic system and also update the model of reduced reference. The reduced model is used to minimize the computational cost and time and also to choose a reduced set of kernel functions. Indeed, the dynamic reduced KPLS allows adaptation of the reduced model, observation by observation, without the risk of losing or deleting important information. For each observation, the update of the model is available if and only if a further normal observation that contains new pertinent information is present. The general principle is to take only the normal and the important new observation in the feature space. Then the reduced set is built for the fault detection in the online phase based on a quadratic prediction error chart. Thereafter, the Tennessee Eastman process and air quality are used to precise the performances of the suggested methods. The simulation results of the dynamic reduced KPLS method are compared with the standard one.

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Section: Air Quality Descriptionmentioning

confidence: 99%

“…e data-driven techniques are characterized by minimal process knowledge and easy implementation using the process historians for model development. Many successes of the data-driven FD are found in several industries, such as chemical industry [3], hydraulic process [4], and kernel methods for image processing [5].…”

Section: Introductionmentioning

confidence: 99%

Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

Saïd

Taouali

2021

Mathematical Problems in Engineering

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“…This approach is not feasible due to the cost and battery requirements, especially in deployments with 100s-1000s of sensors. Recent works [27,38] model 2.5 data to estimate or predict sensor readings using spatio-temporal correlations with adjacent sensors. Any deviation between the sensed and predicated data is categorized as fault.…”

Section: Related Workmentioning

confidence: 99%

CurrentSense

Marathe

Nambi

Swaminathan

et al. 2021

Proceedings of the International Conference on Internet-of-Things Design and Implementation

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Sensor data quality plays a fundamental role in increasing the adoption of IoT devices for environmental data collection. Due to the nature of the deployment, i.e., in-the-wild and in harsh environments, coupled with limitations of low-cost components, sensors are prone to failures. A significant fraction of faults result from drift and catastrophic faults in sensors' sensing components leading to serious data inaccuracies. However, it is challenging to detect faults by analyzing just the sensor data as a faulty sensor data can mimic non-faulty data and an anomalous sensor reading need not represent a faulty data. Existing data-centric approaches rely on additional contextual information or sensor redundancy to detect such faults. This paper presents a systematic approach to detect faults and drifts, by devising a novel sensor fingerprint called Cur-rentSense. CurrentSense captures the electrical characteristics of the hardware components in a sensor, with working, drifted, and faulty sensors having distinct fingerprints. This fingerprint is used to determine the sensors' health, and compensate for drift or diagnose catastrophic faults without any contextual information. The CurrentSense approach is non-intrusive, and can be applied to a wide variety of environmental sensors. We show the working of the proposed approach with the help of air pollution sensors. We perform an extensive evaluation in both controlled setup and real-world deployments with 51 sensors across multiple cities for 8 months period. Our approach outperforms existing anomaly detectors and can detect and isolate faults with an 1 score of 98% and compensate for sensor drift errors by 86%. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems; • Hardware → Fault tolerance.

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“…These methods include Shewhart chart, EWMA chart, cumulative sum chart, and GLRT chart . In previous studies, PCA‐based GLRT, PLS‐based GLRT, kernel PCA–based GLRT, and kernel PLS–based GLRT techniques have been investigated for failure detection in different applications. In Hu et al, a failure diagnosis strategy is realized using parameter‐based PV model obtained by combining energy balance equation and electrical model.…”

Section: Related Work On Failure Detection In Pv Systemsmentioning

confidence: 99%

Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

Mansouri

Hajji

Trabelsi

et al. 2018

Int Trans Electr Energ Syst

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Summary In this paper, a new multiscale weighted generalized likelihood ratio test (MS‐WGLRT) chart is proposed for enhanced failure detection in photovoltaic systems. The main weakness of the classical generalized likelihood ratio test chart is in dealing with residual samples while ignoring their natural variances. By taking into consideration the nature variance of the detection residual and applying a multiscale representation, the proposed technique allows the reduction in false alarm and missed detection rates compared with the classical generalized likelihood ratio test chart. The multiscale representation of data is an efficient data analysis and feature extraction tool that has a great impact on the effectiveness of failure detection. The effectiveness of the proposed method is evaluated on a simulated photovoltaic data where the developed chart is used for detecting single and multiple failures (eg, bypass, mix, and shading failures). The simulation results show that the multiscale weighted generalized likelihood ratio test method offers better performance compared with the classical generalized likelihood ratio chart.

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Enhanced data validation strategy of air quality monitoring network

Cited by 39 publications

References 31 publications

Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

CurrentSense

Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

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