Process trending with piecewise linear smoothing

Mah, Richard S.H.; Tamhane, Ajit C.; Tung, Shih‐Heng; Patel, Aniket

doi:10.1016/0098-1354(94)e0042-l

Cited by 48 publications

(28 citation statements)

References 1 publication

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“…This is similar to the proposals for "signal compression" based on piecewise linear smoothing [12]. However, once a node is put into sleep mode, the sampling is not uniform anymore; this aspect emerged in [11] and is fully considered by the algorithm presented in this work.…”

Section: Energy Saving In Wsnsmentioning

confidence: 77%

DECA: distributed energy conservation algorithm for process reconstruction with bounded relative error in wireless sensor networks

Henriques

Lovisolo²,

Rubinstein³

2016

J Wireless Com Network

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When a wireless sensor network (WSN) is employed to monitor environmental data, efficient energy usage is demanded so that the WSN lifetime can be extended. In this context, we propose a distributed energy conservation algorithm (DECA). DECA considers the known past to make predictions on the measurement change over time in order to put sensor nodes into sleep mode, saving energy. Sensor nodes compute their inactivity periods taking into account the inactivity periods of their neighboring nodes. The signals are reconstructed at the sink node, from the sampled values received from sensor nodes. DECA is designed to keep the reconstruction error of the monitored process below a given fraction of its actual value. Since DECA works on the application layer, it can be combined with medium access control and routing protocols to further improve energy management. The proposed algorithm is evaluated with simulations using real data. Results show gains in the network lifetime of up to 1000 % as compared to a WSN without any energy management, while maintaining the error of the reconstructed process at the sink within the desired fraction imposed. Since a strategy for managing the sleeping period in routing nodes is proposed, a metric for evaluating the effects of sleeping periods on network connectivity is also proposed. We have verified that the network connectivity is not impaired by the proposed algorithm, as packet delivery ratios above 90 % were obtained.

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Section: Energy Saving In Wsnsmentioning

confidence: 77%

DECA: distributed energy conservation algorithm for process reconstruction with bounded relative error in wireless sensor networks

Henriques

Lovisolo²,

Rubinstein³

2016

J Wireless Com Network

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“…Therefore it is also necessary to determine the impact of compression on the observed variance. The measures used are the ratios between the variance of the reconstructed data (r 2 y y ) and the variance of the original data (r 2 y ) (RVC), and between r 2 y and the variance of the reconstruction error r 2 e where e i ¼ y i Àŷ y i (RVE The second of these is similar to the NMSD measure used by Mah et al [8] and Watson et al [9] except that NMSD was expressed as a percentage.…”

Section: Statistical Propertiesmentioning

confidence: 99%

“…Those in industrial use are the direct methods (also known as piecewise linear trending methods) because these can be applied in real-time to spot data. Mah et al [8] and Watson et al [9] have given comparative reviews of various compression methods. Mah et al [8] compared piecewise linear trending methods and introduced a new method (PLOT).…”

Section: Overview Of Data Compressionmentioning

confidence: 99%

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The impact of compression on data-driven process analyses

Thornhill

Choudhury

Shah

2004

Journal of Process Control

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Stored process data in the form of high fidelity time trends are a resource for data-driven process analyses such as statistical monitoring, minimum variance control loop benchmarking, fault detection, data reconciliation and development of inferential sensors. However, many commercial data historians compress the data before archiving it and a question therefore arises of how useful the compressed data are for the intended purposes.This article examines the impact of compression on data-driven methods and presents an automated algorithm by which the presence of piecewise linear compression may be inferred during the pre-processing phase of a data-driven analysis.The results show that compression interferes with many types of data-driven analyses and the paper strongly recommends caution in the use of compressed process data archives.

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“…SDT method is used for industrial flow most because it is easy and simple to be operated, as well as that it can be executed quickly. Signal conversion includes lots of methods, such as DCT (discrete cosine transform) and WT (Wavelet Transform) [5]. Today, WT is being widely used.…”

Section: Introductionmentioning

confidence: 99%

The Research and Improvement of SDT Algorithm for Historical Data in SCADA

Fù

2017

ITM Web Conf.

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Abstract.With the rapid development of Internet of things and big data technology, the amount of data collected by SCADA(Supervisory Control And Data Acquisition)system is growing exponentially, which the traditional SDT algorithm can not meet the requirements of SCADA system for historical data compression. In this paper, ASDT(Advanced SDT) algorithm based on SDT algorithm is proposed and implemented in the Java language, which is based on the deep research of the data compression method, especially the Swing Door Trending. ASDT algorithm through the sine curve fitting data to achieve data compression, compared with the performance of the traditional SDT algorithm, which it can achieve better compression results. The experimental results show that compared with the traditional SDT algorithm, the ASDT algorithm can improve the compression ratio in the case of no significant increase in the compression error, and the compression radio is increased by nearly 50%.

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Process trending with piecewise linear smoothing

Cited by 48 publications

References 1 publication

DECA: distributed energy conservation algorithm for process reconstruction with bounded relative error in wireless sensor networks

DECA: distributed energy conservation algorithm for process reconstruction with bounded relative error in wireless sensor networks

The impact of compression on data-driven process analyses

The Research and Improvement of SDT Algorithm for Historical Data in SCADA

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