Smart proportional electrohydraulic control system :
A pragmatic, robust, and immediately deployable revolution in geotechnical logging
Proportional electrohydraulics, combined with machine learning logic, represents a decisive yet understated evolution in the field of geotechnical drilling. It offers an intelligent, self-regulating, and scalable approach, while building upon the proven architecture of traditional hydraulics.
Technological continuity with a qualitative leap
Our system builds on decades of established drilling practices.
– It maintains comparable measurement protocols, ensuring smooth continuity between historical data and modern campaigns.
– This consistency offers design offices, engineers, and operators a natural transition toward intelligent systems.
A robust, cost-effective, and easily deployable solution
One of the key advantages of this approach lies in its simplicity of integration and low cost:
– It can be adapted to existing machines without requiring a complete overhaul of the mechanical structure.
– It retains the core strengths of hydraulics—power, durability, and adaptability to challenging environments.
– The use of proportional electrohydraulics ensures reduced human error, greater data repeatability, and enhanced precision, all with accessible industrial components in terms of both cost and lead times.
Embedded intelligence for real-time operation
By integrating learning algorithms, the system becomes responsive, adaptive, and capable of optimizing its own settings based on the terrain encountered.
This transforms the drilling machine into an intelligent platform—no longer just an executing tool, but also an analytical and predictive system.
A response tailored to modern requirements
In a context where geotechnical engineering faces challenges related to decarbonization, precision, rapid deployment, and data valorization, this solution offers a coherent, realistic, and scalable response.
The strengths of our technology:
Technical continuity
Compatibility with decades of geotechnical logging data – big data–ready
Ease of deployment
Retrofit on existing machines, rapid implementation
Field robustness
Resilient to harsh environments, high loads, and wear
Error reduction
Less reliance on human input, improved consistency
Economy of scale
Lower costs in scaling AI deployment
Embedded intelligence
Progressive learning, terrain adaptation, and parameter optimization