Predictive maintenance for cyber physical systems using neural network based on deep soft sensor and industrial internet of things

Alassery, Fawaz

doi:10.1016/j.compeleceng.2022.108062

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…The productive technique of fault prediction [1], which provides input data for forecast maintenance of industrial equipment, will also increase the influence of pedagogical models in the formation of professional skills of applicants of higher engineering degree. In general, by accelerating production processes, a large amount of industrial data can also be used in an alternative way.…”

Section: A Expanding the Application Areas Of Industrial Datamentioning

confidence: 99%

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Subject-Information Environment Deployment of Heat Press Operation Hardware Emulation

Neroda¹

2022

Theoretical and Applied Aspects of Device Development on Microcontrollers and FPGAs 2022

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Section: A Expanding the Application Areas Of Industrial Datamentioning

confidence: 99%

“…However, electronics have an advantageeach of the errors can be predicted in advance, and therefore the producer sews in the SPD of the device instructions for notification of the occurrence of a fault to eliminate it in time [1]. The error base is based primarily on the intended purpose of the end device.…”

Section: Mcandfpga-2022mentioning

confidence: 99%

Subject-Information Environment Deployment of Heat Press Operation Hardware Emulation

Neroda¹

2022

Theoretical and Applied Aspects of Device Development on Microcontrollers and FPGAs 2022

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“…Soft sensors (SSs) are extensively applied for monitoring and controlling of technological processes [3][4][5]. In practice, * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach to soft sensor development for distillation-in-series plant under input data low variability

Mozharovskii,

Shevlyagina

2024

Meas. Sci. Technol.

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This paper presents a hybrid approach for integrating fundamental process knowledge with measurement data to soft sensor development with improved estimation capability. Measurement data from sensors are collected and used as inputs for a first-principles model to emulate the data close to restrictions of the operating regulations, thus addressing a low variability problem of the inputs. Next, variables from measurement data and results of the first-principles modeling are combined to extend training dataset for soft sensor which becoming of a hybrid type in nature. To improve an estimation capability, cascade-forward neural network and algorithm for alternating conditional expectation for nonparametric soft sensor development was used. It was shown that the estimation capabilities of the developed soft sensor can be improved by extending the training dataset with first-principles model data approximating the upper and lower limits of the process regime, the amount of which does not exceed 21%. As a result, designed hybrid soft sensor demonstrates a better efficacy in predicting quality index of the targeted distillation product with significantly reduced mean absolute error.

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Hybrid Convolutional Multilayer Perceptron for Cyber Physical Systems (HCMP-CPS)

Pousia¹,

Mythili²,

Kalamani

et al. 2023

Lecture Notes in Networks and Systems

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Predictive maintenance for cyber physical systems using neural network based on deep soft sensor and industrial internet of things

Cited by 11 publications

References 16 publications

Subject-Information Environment Deployment of Heat Press Operation Hardware Emulation

Subject-Information Environment Deployment of Heat Press Operation Hardware Emulation

A hybrid approach to soft sensor development for distillation-in-series plant under input data low variability

Hybrid Convolutional Multilayer Perceptron for Cyber Physical Systems (HCMP-CPS)

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