Missing Value Imputation for Industrial IoT Sensor Data With Large Gaps

Liu, Yuehua; Dillon, Tharam S.; Yu, Wenjin; Rahayu, Wenny; Mostafa, Fahed

doi:10.1109/jiot.2020.2970467

Cited by 78 publications

(43 citation statements)

References 25 publications

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“…unbalanced dataset, noise in the measurements, missing data). Similar to the work proposed by the authors of [25], [3], we also investigate the impact of missing data on the detection of rare events in an industrial setting. The authors of [25] propose a sensor data reconstruction scheme that exploits the hidden data dynamics to accurately estimate the missing measurements.…”

Section: State Of the Artmentioning

confidence: 96%

“…The authors of [25] propose a sensor data reconstruction scheme that exploits the hidden data dynamics to accurately estimate the missing measurements. In [3], the authors focus on missing data imputation for large gaps in univariate time-series data and propose an iterative framework, using multiple segmented gap iteration to provide the most appropriate values. All the approaches mentioned above focus on either data imputation, anomaly detection or fault classification for an industrial process.…”

Section: State Of the Artmentioning

confidence: 99%

“…In the era of IoT big data, the integration of cloud computing technologies and cyber-physical systems enables the full potential of Industry 4.0 to be harvested in manufacturing processes, with a multitude of sensors being installed around the industrial operating environment and equipment. The networked sensors would continuously send monitoring data, allowing for proactive maintenance to take place, leading to a reduction in the unplanned downtime via data analysis techniques [3], [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

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Fault detection and classification in Industrial IoT in case of missing sensor data

Dzaferagic¹,

Marchetti²,

Macaluso³

2021

Preprint

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This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

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Section: State Of the Artmentioning

confidence: 96%

Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fault detection and classification in Industrial IoT in case of missing sensor data

Dzaferagic¹,

Marchetti²,

Macaluso³

2021

Preprint

View full text Add to dashboard Cite

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“…Izonin et al [ 36 ] developed a missing data recovery method by using Adaboost regression on transformed sensor data through Itô decomposition and compared the results with other algorithms like Support Vector Regression (SVR), Stochastic Gradient Descent (SGD) regressor, etc. Liu et al [ 37 ] defined a procedure to deal with large patches of faulty data in uni-variate time-series data. Al-Milli and Almobaideen [ 38 ] proposed a recurrent Jordan neural network with weight optimisation through genetic algorithms.…”

Section: Related Workmentioning

confidence: 99%

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

Iggena¹,

Ilyas²,

Fischer³

et al. 2021

Sensors

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Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

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“…In the coming years of the Internet of Things (IoT), context-awareness bridges the interconnection between the physical world and virtual computing entities, and involves environment sensing, network communication, and data analysis methodologies [1]. Advancement enables several advanced IoT applications, such as intelligent healthcare systems, smart transport systems, smart energy systems and smart buildings.…”

Section: Introductionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

194

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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Missing Value Imputation for Industrial IoT Sensor Data With Large Gaps

Cited by 78 publications

References 25 publications

Fault detection and classification in Industrial IoT in case of missing sensor data

Fault detection and classification in Industrial IoT in case of missing sensor data

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

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