This challenge seeks to detect early the failures of equipment used in production, for example: pumps, motors, valves, etc. It is expected to avoid unforeseen events and stoppages, improving security, reducing down-time and maintenance costs.
The objective is to develop an MVP of a machine learning model with artificial intelligence that allows detecting anomalies or “silent threats” so that they can be incorporated into the maintenance strategy. For this, information from the DCS control systems will be used. The model must be applicable to any valve, motor and pump, taking into account the different operating points of the processes, using clustering techniques.
This challenge is highly scalable as it is geared towards equipment that is widely used in industry.
This challenge seeks to integrate the information from two management systems (dispatch and maintenance) and model the main causes that can impact a risk condition in the operational continuity of its systems or of the entire mobile equipment.
It is expected to improve the useful life and the mean time between failures (MTBF), as well as to optimize the transport cycles and maintain the operational continuity of the equipment, which is highly sophisticated and high volume.
The objective is to generate an MVP of a mathematical model that allows to determine the main causes of unavailability of mobile transport equipment and to characterize in detail the optimal conditions for their continuous operation. For this, information from the fleet management systems (Dispatch-Modular) and maintenance management (Minecare-Modular) will be used.
Predictive management and artificial intelligence help to design a more sustainable mining, increase safety rates, quality and speed of work and standardization of processes. The challenge is to be able to integrate these innovative solutions and for that BHP believes that collaboration is key.
In this second edition of “Hackamine”, BHP seeks to incorporate data analytics technologies that allow optimizing maintenance activities in mine and plant processes and equipment and Artificial Intelligence. For this, it wants to identify agile teams capable of generating a Minimum Viable Product (MVP) of a predictive model that allows early detection of failures to improve safety and productivity in its operation. The solutions are expected to integrate, process, and model possible causes to generate efficient and safe improvements.
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Companies or teams capable of generating a model that supports the maintenance process in a preventive way. We look for the best companies with experience in mining and / or other industries, that have the technological capabilities of Industry 4.0 (Artificial Intelligence, Machine Learning, Big Data, among others) that allow them to develop data analytics solutions to generate intelligence from a large amount of data. In addition, they must be able to market and scale their solutions.
Small and medium-sized companies, local or international startups (with a presence in Chile), local universities and research centers (with alliances with companies or spin-offs for the commercialization of technologies), which have the technological capabilities of Industry 4.0: Artificial Intelligence , Machine Learning, Big Data, among others. * Participation is limited to legal entities.
Access to the challenges of the mining industry.
Advice from mentors with high knowledge of the operation.
Possibility of working with real data from the industry.
Showcase to publicize your technological solutions.
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