The primary function of HERTZINNO acoustic cameras is the visualization of sound waves, converting ultrasonic signals into "acoustic heatmaps" overlaid on optical images. In power station environments, this technology addresses two critical areas:
Partial Discharge (PD) Detection: High-voltage components such as insulators, transformers, and switchgear emit ultrasonic signals during insulation degradation. Acoustic cameras localize these emissions, allowing for the identification of corona, tracking, or arcing without contact.
Pressurized Leak Detection: For facilities utilizing $SF_6$ (Sulfur Hexafluoride) gas-insulated switchgear (GIS), the cameras detect the high-frequency turbulence generated by leaks, enabling remote inspection of energized equipment.
To achieve fully autonomous inspection cycles, HERTZINNO acoustic cameras have been integrated as standard payloads for several prominent quadruped robots, including Deep Robotics and Unitree (B2/Aliengo).
Technical Integration Details:
Payload Mounting: The hardware is integrated via specialized mechanical brackets and powered directly through the robot's internal battery system.
Data Synchronization: Communication is established through standard protocols (e.g., Ethernet/IP or SDK integration), allowing the acoustic data to be synced with the robot’s LiDAR and GPS coordinates.
Multi-Modal Inspection: By combining acoustic imaging with infrared (IR) thermography and high-definition (HD) zoom cameras, the system provides a comprehensive diagnostic profile of the asset.
HERTZINNO solutions are currently utilized in diverse geographical and operational contexts:
Europe: Integration with Unitree for 24/7 autonomous monitoring of indoor high-voltage switchrooms, focusing on mechanical vibration and insulation integrity.
Southeast Asia: Deployment in high-humidity environments where insulation breakdown is frequent. Acoustic cameras are used for wide-area scanning of distribution lines.
Middle East: Monitoring of large-scale solar power plant inverters and transformers, where high ambient temperatures necessitate remote, non-contact diagnostic tools.
The shift toward autonomous "robotic dogs" equipped with acoustic sensors represents a transition from manual, reactive maintenance to automated, predictive maintenance. By filtering out background environmental noise (such as wind or cooling fans), HERTZINNO's algorithms allow for high-precision localization in active industrial sites.
