Big Data-Based Improved Data Acquisition and Storage System for Designing Industrial Data Platform

Geng, Daoqu; Zhang, Chengyun; Xia, Chengjing; Xia, Xue; Liu, Qilin; Fu, Xinshuai

doi:10.1109/access.2019.2909060

Cited by 42 publications

(23 citation statements)

References 31 publications

(22 reference statements)

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“…For example, a third-order tensor A admits a tensor decomposition and its Tucker decomposition result consists of a core tensor G and three left singular value matrices U (1) , U (2) , and U (3) , as shown in Fig. 3.…”

Section: B Tensor Tucker Decomposition Principlementioning

confidence: 99%

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Historical Multi-Station SCADA Data Compression of Distribution Management System Based on Tensor Tucker Decomposition

Zhao

Yan

et al. 2019

IEEE Access

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In order to deal with the problem of massive historical multi-station SCADA data storage in distribution management system, this paper proposes a data compression method for power distribution system based on tensor Tucker decomposition. Firstly, to maintain the high-dimensional spatial structure of multi-station SCADA data of distribution management system in the presentation stage, we establish a thirdorder tensor representation model of multi-station SCADA data. Then, a historical SCADA data compression method for distribution management system based on tensor tucker decomposition is proposed. This method can compress the data while maintaining the spatial intrinsic structure of multi-station SCADA data. Finally, the effectiveness of the method is verified using real data. And, the results of comparison with singular value decomposition method and principal component analysis method show that the proposed method is superior to the traditional method. INDEX TERMS Multi-station SCADA data, tensor tucker decomposition, data compression, power distribution system, spatial intrinsic structure.

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Section: B Tensor Tucker Decomposition Principlementioning

confidence: 99%

“…These data are accumulated over time, putting a lot of pressure on the computer storage space [1]. Therefore, the scheme of using data compression technology to effectively reduce data volume and improve storage efficiency has attracted attention [2].…”

Section: Introductionmentioning

confidence: 99%

Historical Multi-Station SCADA Data Compression of Distribution Management System Based on Tensor Tucker Decomposition

Zhao

Yan

et al. 2019

IEEE Access

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“…Industrial big data [3] has become an important driving force of the new round of industrial revolution. As the most practical and highest frequency demand of industrial big data in the production process, data acquisition [4][5][6] is also used as one of the measurement standards of industrial field automation integration. Data collection is widely used in many fields such as signal detection, equipment monitoring, signal processing and instrument detection.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Adaptive Sampling Method Based on Edge Computing

Lou

Shi

Zhang

et al. 2020

Sensors

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The rise of edge computing has promoted the development of the industrial internet of things (IIoT). Supported by edge computing technology, data acquisition can also support more complex and perfect application requirements in industrial field. Most of traditional sampling methods use constant sampling frequency and ignore the impact of changes of sampling objects during the data acquisition. For the problem of sampling distortion, edge data redundancy and energy consumption caused by constant sampling frequency of sensors in the IIoT, a data-driven adaptive sampling method based on edge computing is proposed in this paper. The method uses the latest data collected by the sensors at the edge node for linear fitting and adjusts the next sampling frequency according to the linear median jitter sum and adaptive sampling strategy. An edge data acquisition platform is established to verify the validity of the method. According to the experimental results, the proposed method is more effective than other adaptive sampling methods. Compared with constant sampling frequency, the proposed method can reduce the edge data redundancy and energy consumption by more than 13.92% and 12.86%, respectively.

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“…This middleware can be represented by the fog computing [11], which is capable of providing local computing support. On the other hand, the large amount of generated data requires Big Data technologies in the industry, and the paper from [12] presents a survey of the Big Data solutions that are already integrated into the industry, while [13] focuses on identifying the high performing Big Data techniques that can be applied to industrial data.…”

Section: Introductionmentioning

confidence: 99%

Weather-Based Prediction Strategy inside the Proactive Historian with Application in Wastewater Treatment Plants

2020

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The current landscape in the water industry is dominated by legacy technical systems that are inefficient and unoptimized. In recent years, sustained efforts could be identified, especially under the guidance of the Industrial Internet of Things (IIoT) paradigm, in order to develop an increased level of both connectivity and intelligence in the functioning of industrial processes. This led to the emergence of the data accumulation concept, materialized in the practical sphere by Historian applications. Although various classic Historian solutions are available, the capability to optimize and influence the monitored system in a proactive way, resulting in increased efficiency, cost reduction, or quality indicators improvements, could not be identified to date. Following a proposed software reference architecture for such a proactive Historian, a data dependency identification strategy and some obtained recipes for energy efficiency improvements in the water industry were developed. However, a complete solution for real industrial processes represents complex research. The current paper contributes to this research effort by developing part of the reference architecture that predicts the future evolution of the monitored system, based on weather dependency and forecast, thus sustaining the effort to achieve a fully functional, real-world, tested and validated proactive Historian application, with potential to bring significant direct benefits to the water industry.

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Big Data-Based Improved Data Acquisition and Storage System for Designing Industrial Data Platform

Cited by 42 publications

References 31 publications

Historical Multi-Station SCADA Data Compression of Distribution Management System Based on Tensor Tucker Decomposition

Historical Multi-Station SCADA Data Compression of Distribution Management System Based on Tensor Tucker Decomposition

A Data-Driven Adaptive Sampling Method Based on Edge Computing

Weather-Based Prediction Strategy inside the Proactive Historian with Application in Wastewater Treatment Plants

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