Explosion-proof Online Oil Monitoring System - SKA IOL-EX2 Series

SKA IOL-EX2 Series

Explosion-proof Online Oil Monitoring System

>> System Introduction

The explosion-proof online oil monitoring system integrates multi-parameter sensor monitoring such as physical and chemical properties and contamination level. The system is equipped with a 7-inch high-definition explosion-proof screen. Through internal sensor integration, the system can achieve real-time monitoring of oil status. The equipment as a whole adopts explosion-proof housing and circuit design, and internal sensors and other components all adopt intrinsically safe circuit design, allowing safe use in complex industrial sites such as dust, petrochemical, and flammable gas environments.

◼ Newly self-developed resistivity monitoring technology (new)

Resistivity measurement technology directly targets the core of oil quality. Through precise analysis of dissolved acidic substances and other soluble contaminants in oil, the conductive ion content is analyzed, and physical and chemical indicators such as resistivity (Ω·cm) and temperature (°C) are monitored online.

◼ Dynamic image particle monitoring technology (new)

Intelligently monitors the contamination status in oil, effectively identifies bubbles, moisture, fibers, impurities, and acid value in oil; and can classify and output oil contamination to achieve predictive maintenance for equipment, outputting dynamic image data in as fast as 2s and outputting a test result in as fast as 30s.

• The system directly outputs kinematic viscosity at 40°C, oil quality, contamination level, water content, saturation, wear particles, enabling comprehensive monitoring of equipment oil aging status.

• Built-in over 200 oil models, adapting to different industrial sites and different oil types.

• Multi-parameter fusion technology, capable of collecting pressure, flow, vibration and other data, assisting in achieving scientific monitoring.

• The system possesses multi-method fusion technology and AI autonomous learning capability. conditions.

  • Multi-leak monitoring: prevents leakage of the system itself, enables automatic control of start and stop

  • Electromagnetic opening and closing: based on the system operation model, autonomously learns the working patterns of the monitored equipment

  • Software self-check: autonomously connects with cloud platform, regularly achieves software version updates

  • Flow control: adapts to different on-site working condition changes, maintains constant system pipeline flow