Friday, 24 January 2020
MRIWA Tech Talks - Smart shovels and Wearable tech to prevent injury

MRIWA Tech Talks - Smart shovels and Wearable tech to prevent injury

As part of Data Science Week, MRIWA will present two tech talks involving the newest applications of sensor technology in the mining industry. Held at the Pawsey Supercomputing Centre, there will be opportunities to view the supercomputers before and after the talks. 

PART ONE 2:45 - 3:45pm - Predictive maintenance for humans

Workplace injuries result in recovery and compensation costs, while also having a large impact on productivity due to resultant staffing shortages and incident investigation. Likewise, machine breakdowns also have significant maintenance and lost productivity costs.

Sophisticated organisations no longer wait for machine breakdowns, but rather analyse data and predict component failure and implement maintenance strategies to avoid this from occurring. Soter Analytics helps organisations by applying the same methodology to avoid workplace injuries.

SoterSpine helps workers and organisations by avoiding musculoskeletal injuries, the most expensive injury category for industrial companies. A wearable device measures and quantifies worker risk, calculates the probability of injury, and then provides personalised coaching to the worker to reduce their own risk of injury. Aggregated analysis is also provided to the organisation to make system level changes, engineering risk out of the workplace.

PART TWO 3:45 - 4:45pm - Big data making smart shovels and reality

In the mining industry, the implementation of autonomous equipment is gathering pace.

One significant potential application of autonomous mining equipment, is in the implementation of a fully autonomous mining shovel.

A great deal of progress has already been made in the realisation of a fully autonomous mining shovel, such as Semi-Autonomous Load Assist (SLAP), Truck Shield, and Dozer shield technologies.

However, gaps still exist.

These gaps primarily revolve around the application of disparate sensors and large sets of data, in real-time.

The application and integration of these data-sets, offers the ability to sense and respond to terrain and material characteristics in real-time.

Additionally, the use of these data-sets would be able to support the integration of autonomous shovels with mine scheduling processes.

This presentation highlights the current state of the art in the implementation of a fully autonomous shovel, and offers a path forward to close the remaining gaps.


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