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  6. Prediction of Failure Timing Using Vibration Data

Prediction of Failure Timing Using Vibration Data

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last updated:Dec 13, 2021

ウェーブフロント 本社
ウェーブフロント 本社
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Use the data at hand for failure prediction and quality improvement!

"Prediction of Failure Timing Using Vibration Data" is our company's data mining and consulting service. In vibration data analysis, several implementation methods can be assumed based on the monitoring methods and the data you have. This document clearly introduces the prediction methods using vibration data and user case studies. *For more details, please refer to the catalog or feel free to contact us.*

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Prediction of Failure Timing Using Vibration Data

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basic information

[Analysis Policy] - Correlation analysis based on regular measurements - Correlation analysis based on continuous measurements - Analysis based on failure history *For more details, please refer to the catalog or feel free to contact us.

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It can be utilized in various scenarios such as fault prediction from motor vibration values!

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Failure timing prediction using vibration data

Failure timing prediction using vibration data

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ウェーブフロント

ウェーブフロント 本社

software

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Our company develops and sells a "Maintenance Management System" for managing and operating various plants, factories, and other facilities and assets. Currently, this system is undergoing significant evolution into one that incorporates IoT technologies, such as sensor information and input from tablet devices, as well as AI technologies like machine learning, featuring functions for failure prediction and automatic scheduling. Additionally, as part of the recent trend towards digital transformation (DX), there is a growing movement to digitize and automate manufacturing processes and research and development sites in factories to enhance operational efficiency. In line with this trend, our company provides a solution aimed at improving efficiency in research and development environments, known as the Laboratory Information Management System (LIMS), which includes features such as workflow management, data tracking, data management, data analysis, and integration of electronic lab notebooks.

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