Online Auxiliary Equipment Monitoring_Shenzhen Qianhai IntelliUnion Technology Development Co., Lt

Online Auxiliary Equipment Monitoring

Critical thermal power auxiliaries, including forced-/induced-draft fans, compressors and feedwater pumps, operate under heavy loads, strong vibration and dust. Wear, sticking and parameter abnormalities are common, and single-point faults can cascade into main-unit shutdowns and major losses. Traditional delayed monitoring and warnings no longer meet precise plant operation needs. This solution deploys accurate sensors and intelligent acquisition/analysis for 24/7 vibration, temperature and displacement monitoring. Diagnostic algorithms detect early faults and predict deterioration, while seamless integration with existing controls supports proactive predictive maintenance, reduces shutdown risks, extends equipment life and improves safety and economics.

Monitoring Challenges

O&M Efficiency Challenges

Manual inspections are demanding and troubleshooting depends on experience, reducing efficiency. Hot and dusty areas pose significant safety risks.

Lack of Continuous Monitoring

Without continuous online monitoring of critical auxiliary equipment, operating history cannot be fully traced. Early bearing wear and winding abnormalities may go undetected, causing unplanned downtime and economic losses.

Insufficient System Expandability

Isolated systems and inconsistent protocols limit compatibility with different sensors and expansion modules. Equipment upgrades and new monitoring needs require difficult, expensive modifications.

Weak O&M Warning Capability

Without predictive maintenance, repairs rely on experience, with both over- and under-maintenance. Fixed thresholds cannot learn or adapt to changing conditions, causing frequent false and missed alarms.

Product Advantages

Real-Time Monitoring Advantages

Continuous 24-hour monitoring detects abnormalities and provides early warnings to prevent escalation and maintain power supply continuity.

Intelligent Early Warning and Diagnosis

Self-learning alarms adapt to baselines and distinguish normal fluctuations from faults, substantially reducing false alarms. Real-time analysis supports warning, localization and deterioration assessment to enable predictive maintenance.

Rich Interfaces and Broad Applicability

Gigabit Ethernet, RS485 and Type-C debugging interfaces enable convenient integration, rapid deployment and compatibility with diverse units and systems.

Data Traceability and Health Assessment

Historical data, fault recordings and O&M reports are traceable. Equipment health models provide deterioration trends and remaining-life recommendations.