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Fault Prediction × IoT - Building a Predictive Maintenance System

Introducing the construction of predictive maintenance systems, including system images and case studies!

The background for the growing attention on anomaly detection and predictive maintenance includes factors such as the increasing complexity of failures and the rising costs of maintenance. This document introduces the construction of predictive maintenance systems. Additionally, it explains the image of predictive maintenance systems, case studies of their construction, and the construction workflow. 【Contents (Excerpt)】 ■ Image of Predictive Maintenance System ■ Predictive Maintenance by Three "Functional Levels" ■ Background for the Attention on Predictive Maintenance ■ Case Studies of Predictive Maintenance System Construction ■ Workflow for Constructing Predictive Maintenance Systems *For more details, please refer to the PDF document or feel free to contact us.

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  • Software (middle, driver, security, etc.)

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LogP prediction, LogD prediction 'Partitioning'

Predictions will be made based on the improved methods of Viswanadhan for both logP and logD!

The "Partitioning Group" can predict the pH-dependent logD value in addition to the water/octanol partition coefficient. Predictions are based on the improved methods of Viswanadhan et al. for both logP and logD. The modifications applied include the redefinition of selected atom types (particularly sulfur, carbon, nitrogen, and metal atoms) to adjust for electronic delocalization and consideration of ionization forms. 【Features】 ■ Predicts pH-dependent logD values in addition to the water/octanol partition coefficient ■ Predictions are based on the improved methods of Viswanadhan et al. for both logP and logD ■ The logP value of zwitterions is calculated from logD at the isoelectric point ■ Just as logD values are pH-dependent, logD calculations rely on methods for predicting pKa *For more details, please refer to the PDF document or feel free to contact us.

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