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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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  • Failure prediction

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Fault prediction using AI-FTA for power generation.

Accelerate the identification of the causes of power generation equipment failures and support stable operation.

In the power generation industry, stable operation of equipment is essential, and downtime due to failures can lead to significant losses. Particularly with complex equipment, identifying the cause of a failure can take time, and delays in response can result in further damage or prolonged outages. AI-FTA enables rapid responses by integrating on-site data and assisting in narrowing down the causes of failures. 【Usage Scenarios】 - Power plants - Major equipment such as turbines, boilers, and generators - Identifying causes during failure occurrences 【Benefits of Implementation】 - Reduction in time to identify failure causes - Decrease in the risk of equipment downtime - Assetization of trouble response history

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  • Failure prediction

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