I. Introduction: Maintenance Challenges of Large-Scale Experimental Equipment   Large-scale experimental equipment is critical in modern scientific research and industrial production—high-value, complex, and technically sophisticated. Their stable operation directly impacts research progress, product quality, and manufacturing efficiency. However, traditional maintenance strategies are increasingly inadequate, driving the industry to explore intelligent upgrade pathways.   II. Limitations of Traditional Maintenance Strategies   Traditional maintenance primarily relies on two approaches, both with significant drawbacks:   1. Preventive Maintenance   Involves periodic servicing based on time/usage cycles, aiming to prevent failures via routine inspections and component replacements.   Issues: Risk of over-maintenance (wasting resources) or under-maintenance (failing to address hidden faults); cannot accurately reflect equipment health status.   2. Corrective Maintenance   Occurs only after equipment failure, leading to:   Production interruptions, data loss, and potential secondary damages;   High operational costs due to unplanned downtime.   III. The Core of Intelligent Maintenance: Data-Driven Predictive Maintenance (PdM)   To address these challenges, intelligent maintenance integrates IoT, big data, and AI to build a Predictive Maintenance (PdM) system, following three key steps:   1. Real-Time Data Collection: High-Precision Sensor Deployment   High-precision sensors (vibration, temperature, pressure, current, acoustic) are installed on key equipment components to monitor operational data in real time. Data is transmitted via IoT networks to cloud platforms or local servers.   2. Data Processing: Big Data Analytics for Feature Extraction   Raw data is processed, cleaned, and integrated using big data techniques to extract valuable features and patterns (e.g., abnormal vibration frequencies, temperature trends).   3. Fault Prediction: AI Algorithm Implementation   Machine learning models (support vector machines, neural networks, deep learning) are trained on historical data to recognize "healthy" states and early failure signs. For example:   Analyzing abnormal vibration patterns to predict mechanical wear;   Monitoring temperature fluctuations to detect electrical faults.   IV. Advanced Technologies: Digital Twin and AR for Enhanced Maintenance   Intelligent maintenance is further empowered by cutting-edge technologies like digital twin and augmented reality (AR):   1. Digital Twin Technology   Creates virtual models of equipment synchronized with real-time data from physical assets. Operators can:   Simulate operation scenarios;   Diagnose faults remotely;   Optimize maintenance procedures.   2. AR Technology   Onsite maintenance personnel use AR glasses to access:   Real-time equipment information;   Step-by-step operational instructions;   Remote guidance from experts.   V. Advantages and Implementation Challenges of Intelligent Maintenance   1. Core Advantages   Reduces failure rates and unexpected downtime;   Extends equipment service life;   Lowers operational costs;   Optimizes resource allocation;   Ensures data integrity in experiments/production.   2. Implementation Challenges   High initial investment in sensors, AI systems, and training;   Data security risks (e.g., cyberattacks on sensitive operational data).   VI. Conclusion: Intelligent Maintenance as an Inevitable Trend   Despite challenges, intelligent maintenance is critical for achieving high efficiency, reliability, and sustainability of large-scale experimental equipment. It is a strategic choice to enhance core competitiveness in scientific research and industrial fields, marking an inevitable shift from reactive to proactive maintenance.   Disclaimer: This website respects intellectual property rights. If any infringement is found, please contact this website in a timely manner for handling.