Cloud Internet of Things Based Machine Monitoring Analysis of Energy Parameters Using Novel Techniques

Agarwal, Ankit

doi:10.1007/s11277-021-09431-x

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Collect the analog signal output by the sensor and transmit it to the external network or send the information to the control node through wired to the PC. After obtaining the digital sequence code, the desired result is parsed through a series of operations such as preset placement, and stored in the server for subsequent use or retrieval of other service resources [11][12].…”

Section: Cloud Monitoring Platform Integrating Machine Learningmentioning

confidence: 99%

Cloud Monitoring Data Analysis and Simulation Based on Machine Learning

2022

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With the rapid development of Internet technology, cloud computing has gradually been widely used in people's lives. However, due to the huge and complex data volume, limited storage space and maintenance difficulties, there are still some problems in cloud monitoring data analysis. This paper intends to use the combination of machine learning to study the analysis and experimental simulation of cloud monitoring data, in order to improve the data processing ability of the monitoring system. This paper mainly uses the experimental analysis method and information entropy to study the cloud monitoring system and its data processing and analysis capabilities. The experimental data show that the error of computer tools is less than 6%, which shows that the method is feasible and worthy of application.

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Section: Cloud Monitoring Platform Integrating Machine Learningmentioning

confidence: 99%

Cloud Monitoring Data Analysis and Simulation Based on Machine Learning

2022

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“…Recently, owing to the effects of the global coronavirus pandemic and decline in the working-age population in some countries, production e ciency and labor productivity are being actively improved at manufacturing and production sites by introducing digital technologies. Digital transformation (DX), Internet of Things (IoT) utilization, digital twin application, etc., are the representative efforts being made in the manufacturing and production elds these days [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

APVC2021 Automated failure diagnostic system for a cylinder of the hydraulic pressing machine

Terashima

Takeda

et al. 2023

Preprint

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Due to the impact of the COVID-19 pandemic and declining working-age populations in certain countries, manufacturing and production sites have actively enhanced production efficiency and labor productivity by incorporating digital technologies. In light of these trends, we have developed a system aimed at swiftly assessing the operational status of machinery in order to optimize production efficiency within a manufacturing company, thereby reducing the time required for machine inspection and repair. To efficiently identify failures and anomalies in hydraulic presses, we have implemented a machine learning-based failure diagnostic system. Vibrational acceleration sensors were installed on the cylinder, the primary component of the press machine, and continuous signal data was collected. By establishing a model based on standard deviation, crest factor, and maximum signal values for normal operation, deviations and temporal changes in the data were utilized to detect failures and anomalies. Consequently, it is feasible to predict the occurrence of failures by monitoring these variations.

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