Key Components of Predictive Maintenance

Predictive maintenance is a data-driven closed-loop system. Its core objective is to accurately predict equipment failures. Key components include four modules: data acquisition, transmission and processing, analysis and modeling, and decision execution. Efficient predictive maintenance requires the coordinated operation of each module.

1. Status Awareness Layer: Data Acquisition Module

This module collects real-time physical status data of the equipment, forming the foundation of predictive maintenance. The core carriers are various industrial sensors.


2. Network Transmission Layer: Data Transmission and Edge Processing Module

This module securely and in real-time transmits the collected raw data to the analysis platform and performs preliminary screening to reduce cloud computing load.


3. Intelligent Analysis Layer: Data Storage and Modeling Analysis Module

This module is the core decision-making unit for predictive maintenance. Through analysis of historical and real-time data, it predicts equipment failure trends and remaining service life.


4. Execution Application Layer: Decision and Operation Management Module

This module transforms the analysis results into actionable maintenance actions, forming a closed loop of "data acquisition - analysis and early warning - maintenance execution - effect feedback."

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