Forecasting complex multi-component time series within systems designed to detect anomalies in dataflows of industrial automated systems

Ragozin, Andrey N.; Telezhkin, Vladimir; Podkorytov, P. S.

doi:10.1145/3357613.3357615

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…В данном случае рассматривается режим обучения «частично с учителем». Исходные данные при таком подходе представляют только класс «нормальных данных», при этом, при обучении «частично с учителем» не требуется информация об аномальном классе целевых данных Очевидно, что точность настройки формируемого прогноза наблюдаемого процесса АСУ ТП, работающей в штатном режиме играет важную роль, так как в данном случае формируемый прогноз наблюдаемого процесса АСУ ТП является эталонной моделью процесса АСУ ТП, работающей в штатном режиме [8].…”

Section: The Use Of Digital Signal Processing and A Neural Network When Generating A Forecast Of Time Series Of Data For The Purpose Of Dunclassified

The Use of Digital Signal Processing and a Neural Network When Generating a Forecast of Time Series of Data for the Purpose of Detecting Anomalies in the in the Automated Control of Technological Processes

Ragozin¹

2020

Self Cite

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n order to detect anomalies and improve the quality of forecasting dynamic data flows observed from sensors in Industrial Control System (ACS)., it is proposed to use a predictive mod-ule consisting of a series-connected digital signal processing unit (DSP) and a predictive unit using a neural network (predictive autoencoder ( Auto Encoder), predictive Autoencoder (PAE)). The study showed that the preliminary DSP block of the predicted input signal, consisting of a parallel set (comb) of digital low-pass filters with finite impulse responses (FIR-LPF), leads to a non-equilibrium account of the correlation relationships of the time samples of the input signal and to increase the accuracy of the final prediction result. The predicted autoencoder (PAE) pro-posed and considered in the work, in addition to restoring the input signal or part of the input signal at the PAE output, also generates the predicted samples of the input signal for the speci-fied number of «forward» time steps at the output, which increases the accuracy of the predic-tion result. The reduction of the forecast error occurs due to the imposition of restrictions in the formation of the forecast, that is, an additional requirement to restore the input samples of the samples – «stabilizers» at the NS output. The introduction of «stabilizers» increases the accuracy of the prediction result.

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Section: The Use Of Digital Signal Processing and A Neural Network When Generating A Forecast Of Time Series Of Data For The Purpose Of Dunclassified

The Use of Digital Signal Processing and a Neural Network When Generating a Forecast of Time Series of Data for the Purpose of Detecting Anomalies in the in the Automated Control of Technological Processes

Ragozin¹

2020

Self Cite

View full text Add to dashboard Cite

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Application of the Coherent Spectrum Method with a Reference Signal in Problems of Diagnostics the State of Technical Equipment

Ragozin

Pletenkova

2020

2020 Global Smart Industry Conference (GloSIC)

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Artificial Neural Network Predictive Autoencoder with Pre-Digital Signal Processing Unit

Ragozin

Pletenkova

2023

2023 International Russian Smart Industry Conference (SmartIndustryCon)

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Forecasting complex multi-component time series within systems designed to detect anomalies in dataflows of industrial automated systems

Cited by 3 publications

References 11 publications

The Use of Digital Signal Processing and a Neural Network When Generating a Forecast of Time Series of Data for the Purpose of Detecting Anomalies in the in the Automated Control of Technological Processes

The Use of Digital Signal Processing and a Neural Network When Generating a Forecast of Time Series of Data for the Purpose of Detecting Anomalies in the in the Automated Control of Technological Processes

Application of the Coherent Spectrum Method with a Reference Signal in Problems of Diagnostics the State of Technical Equipment

Artificial Neural Network Predictive Autoencoder with Pre-Digital Signal Processing Unit

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