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Flying High

23rd October 2018


Drone technology can predict the failure of mining machinery

Internationally renowned drones expert Robert Garbett explores how mining technology is driving the evolution of the drone industry

The mining industry has long embraced the use of advanced technology in its drive towards greater safety, efficiency and profitability and spending in this area is set to increase by 20% in 2018.1 What many people may not realise, however, is how technological advances made in the mining industry are now solving issues in and driving the evolution of the drone industry. One example of note is technology being developed by Precise Prediction, a Norwegian company that has been developing predictive analytics solutions, advanced sensor and machine learning tools for the diamond mining industry since 2006. Such technology has been widely used to predict the failure of mining machinery for some time but could now be employed to predict the failure of drone motors, solving many of the safety concerns that exist around the use of air drones globally.

Solving the ‘fail safe’ challenge

One key issue with air drones or unmanned aircraft (UA) is that they have an extremely short lifespan when compared with manned aircraft and so it is impractical to expect manufacturers to adhere to strict aviation certification standards. It has to be assumed that components on a UA are more likely to fail than those used in a traditional manned aircraft, which poses specific problems for their acceptance by regulators for wider use. This issue is being examined at a high level through the development of robust international standards by the International Standards Organisation (ISO). The very first drone standards are now being developed by the ISO and are due for committee draft release in the spring of 2019.

A key aim of these standards is to address the minimum safety quality standard required when manufacturing an unmanned air system (UAS). One consideration raised during development is the need for the drone to be able to ‘fail safe’. And although the standards will not dictate how a manufacturer should solve this problem, in the likely event that this requirement is included in the standard, it leaves the manufacturer with a serious issue of how this can be achieved. In the past, solutions such as the use of parachutes have been introduced by safety-conscious manufacturers but, with advancements in predictive maintenance solutions, manufacturers now have an alternative that would ensure that the UA returned to the operator prior to motor failure and indicate which motor needs to be changed. This simple but effective solution, born in the diamond mining industry, will enable the air drone industry to satisfy the essential ‘fail safe’ requirement thus opening the doors to far wider applications of this revolutionary technology.
 
Additional development could extend this to other areas of a UAS to further enhance safety in flight, especially in manned drone systems, which are set to revolutionise the way in which we transport freight and people in the future. This development alone could accelerate the growth of the drone industry considerably, leading to a substantial expansion in the introduction of air transport drone systems globally.

The power to think on the fly

A common use of drone technology is in the analysis of geographical information and the test and inspection of safety-critical systems such as power installations, pipelines, oil platforms and infrastructure such as bridges, buildings and dams. The process has, until now, required the drone to complete its operation and return the data to the operator, which is then often sent to a third-party supplier for processing. The supplier would than have to analyse it before any action could be taken on its findings. This is somewhat of an Achilles heel for the drone industry as it introduces a considerable delay in the process and risks the situation changing before corrective action can be taken. It also introduces additional costs into the process, often making the margin between using drones for inspections and using human inspectors negligible. Machine learning technology developed by Precise Prediction can enable a drone to process imagery to detect levels of rust, cracks, damage and other issues live! The ‘learning drone’ will be able to process and determine the nature and severity of the damage while it is in the air – which is a remarkable capability enhancement.  

The cost savings alone made by this development make the use of drone inspection systems over human inspectors compelling. In addition, this technology will undoubtedly lead to a new race of ‘on drone’ repair systems that could take corrective actions immediately or enable the dispatch of secondary repair drones immediately. Although this technology is still young, it represents a quantum leap for the drone ‘test and inspect’ industry, essentially evolving it to ‘test, inspect and repair’ and leading to new developments in ‘on drone’ repair systems capable of a wider range of rectification. This one development gives this sector impressive potential for growth.

The bigger picture

Drone Major continues to work closely with the mining industry to provide guidance on the use of advanced and innovative drone technology and the organisation is delighted when it is able to take great ideas born in the mining sector into the drone industry for the benefit of all concerned.







 

 

 

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