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Case study

Proximity awareness system

In 2022, our team at Curragh conducted an engineering study to identify risk control opportunities that would decrease the likelihood of machine-to-machine and machine-to-personnel interactions by applying appropriate detection technologies. At the time of the review, personnel proximity detection systems compliant with a functional safety standard had not been established for large machinery in Australia.

A comprehensive analysis of available or in-development technologies was conducted to determine their potential for achieving a functional safety rating, initially in a dragline operational context. The project devised a system that layers three distinct technologies with different sensing ranges:

  1. Cameras (long range)
  2. LiDAR (medium range)
  3. Radar (short range).

The LiDAR and cameras use two distinct artificial intelligence systems specifically trained to handle the unique environments of a mining operation. Each layer of technology serves a specific purpose in the system:

  1. Cameras provide operators with notifications and allow them to track personnel/vehicles around the dragline from the boom point inwards, creating a heightened sense of awareness.

  2. If the LIDAR detects the presence of a person, it alerts the operator and actively clamps the swing function based on the distance from the dragline. The clamp increases the closer the person is to the dragline, until the machine comes to a stop before the next layer is triggered.

  3. The radar triggers a stopping function, further alerting the operator and initiating lock-out swing functionality with reset procedures, ensuring compliance with the machine and personnel interaction boundaries.

This layered approach using disparate technologies has effectively reduced the risk of personnel or equipment interactions with draglines, creating a safer working environment. The versatility of this scalable approach makes it applicable to other machinery within the mining industry where similar risks exist.