With the maturity of IoT and big data technologies, intelligent monitoring platforms have become central to fan maintenance. This article explains how online monitoring, data analysis, and predictive models enable early fault warning and optimized O&M decisions, focusing on system architecture, key monitoring parameters, application cases, and implementation essentials.
1. System Architecture & Key Components
Sensor Layer:
Vibration sensors capture bearing and casing vibration signals.
Temperature sensors monitor bearings, windings, and ambient temperatures.
Pressure/flow sensors record real-time duct pressure and airflow changes.
Sound level meters detect noise anomalies.
Edge Gateway:
Handles initial data validation, compression, and encryption; supports industrial protocols like Modbus and OPC UA.
Cloud Platform & Big Data:
Time-series databases efficiently store large volumes of sensor data.
Data warehouse integrates historical operation, maintenance, and external conditions.
Analytics & Visualization:
Dashboards for real-time KPI display.
Alarm systems with graded alerts by thresholds/rules.
Machine learning modules (e.g., vibration spectrum, temperature rise) to build fault prediction models.
2. Core Monitoring Parameters & Fault Warning
| Parameter | Fault Type | Warning Basis |
|---|---|---|
| Vibration amplitude | Bearing imbalance, gear damage | Acceleration/displacement exceeds threshold |
| Frequency spectrum | Misalignment, looseness | Feature peaks appear in the spectrum |
| Bearing temperature | Insufficient lubrication, overload | Temperature rise exceeds set gradient |
| Duct pressure/flow | Leakage, valve fault | Measured flow/pressure deviates ≥10% from design |
| Noise level | Aero-separation, mechanical friction | Sharp increase or specific frequency peak |
Thresholds: Refer to ISO 10816, ISO 14694, and equipment manuals for tuning.
Model Training: Train with normal/fault data using Random Forest, LSTM, etc., to achieve 48–72 hour advance warning.
3. Application Cases
Mine Main Fan Predictive Maintenance:
Online vibration & temperature monitoring + LSTM model gave 60-hour advance warning of bearing failure, avoiding unplanned shutdown.Metallurgy Plant Fan Efficiency Optimization:
Monitoring flow/power ratio identified optimal energy-saving points, improving system efficiency by 8% and saving 150,000 kWh/year.Tunnel Emergency Fan Lifecycle Management:
Integrated maintenance records with online data for health assessment and dynamic maintenance scheduling, cutting repair costs by 20%.
4. Implementation Essentials & Best Practices
Pre-survey: Assess on-site network and power, select industrial-grade sensors/gateways.
Phased Deployment: Pilot single machines before full-plant rollout; refine alarm rules and models iteratively.
Data Governance: Standardize tags, clean missing/abnormal data, ensure model training and online prediction accuracy.
O&M Coordination: Build a joint O&M-IT team, define clear response processes and authority, conduct regular drills.
Continuous Optimization: Review warning performance, update algorithms/thresholds, and expand platform functions for new equipment/scenarios.