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Uptake, Symboticware to provide miners with predictive maintenance solution

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Uptake says it has partnered with Symboticware to provide mining companies with an end-to-end, integrated artificial intelligence (AI) and data science solution to increase the productivity of mobile mining equipment.

The joint solution combines Symboticware’s SymBot® device, which provides data capture from mining fleets, and Uptake’s Asset Performance Management (APM) software, Asset IO, which applies AI to surface predictive insights from the data.

The combination of the two, according to Uptake, arms users with:

  • Data-driven cost reductions: For an average medium-sized mining site, Asset IO can save as much as $2.5 million/y in haul truck maintenance, fuel and tyre costs alone. Additionally, operator and fleet productivity is optimised with the help of benchmarking and informed decision-making;
  • Actionable insights for continuous improvement: Asset IO quickly turns data into actionable, AI-driven insights. The software deploys AI models that are pre-trained to drive greater performance, reliability and availability of the mobile mining fleet; and
  • Greater visibility into assets: collect data from sensors, store time-stamped values in an internal database and seamlessly transmit data for AI analysis.

Ash Agarwal, Director of Mining at Uptake, said: “Optimal machine health and performance is critical to getting the most out of investments in equipment, and to achieving a high return on capital employed. Performing regular maintenance on haul trucks can be labour- and time-intensive, with the added cost of unplanned downtime having a significant impact on throughput and operating expenses.

“This integrated solution provides the best of industrial IoT and AI to reduce downtime, minimise operating expenses and aid in the development of a comprehensive maintenance program.”

Kirk Petroski, President and CEO of Symboticware, said industrial data is only as good as the insights that can be gleaned from it. “This solution delivers predictive insights that are both accurate and actionable, providing users with sufficient lead time into maintenance issues so they can be ahead of the curve. That ability to proactively mitigate maintenance problems yields a proven, measurable increase in productivity,” he said.

GMG expands AI and automation focus with new projects

Automation, Equipment maintenance, IoT, Mining equipment, Mining maintenance, Mining safety, Mining services, Mining software, , , , , , , ,

The Global Mining Guidelines Group (GMG) has launched new projects in the fields of artificial intelligence and autonomous equipment to ensure mining companies can best leverage these technologies.

The ‘Open Data sets for AI in Mining’ project will be used for building open data sets to advance AI research and development, while the ‘Autonomous System Safety’ sub-project (under the Functional Safety for Autonomous Equipment project) looks to deliver valuable context and education on system safety, GMG said.

As GMG states: “Open and curated data sets can enhance the ability to build meaningful solutions for the industry by providing typical data relating to assets or operations for training and testing models and improving benchmarking and research by offering an alternative to proprietary data.”

The open data sets project will seek to leverage what the wider AI community has learned over time and ensure the approaches used in the mining domain are consistent with best practices, it added.

In terms of deliverables, the GMG is hoping for three core outcomes. Namely, a register of suitable candidate data sets, a set of guidelines for the collection and curation of these data sets and a set of repositories of gathered data.

“AI research and progress in many spheres has benefited hugely from having a set of public and curated datasets,” GMG said. “This has allowed for developers and researchers to have suitable data to test and train their models on for a variety of applications. Even more importantly, it has provided data which can be used to benchmark various solutions and allow for effective and fair comparison, as well as allowing for research to be repeated and validated.”

The ‘Autonomous Safety System’ sub-project, meanwhile, covers overall system safety. It will be a white paper to “provide valuable context and education on system safety, its history in other industries and how to deliver safe systems that can be operated effectively”, according to GMG.

The GMG said: “An outlook that expands the focus from functional safety to system safety will enable improved outcomes to the delivery of autonomous mining systems because:

  • To ensure functional safety, autonomous systems need to perform their functions correctly;
  • A technological system and its design within the operating environment can influence human performance;
  • Delivering and benefiting from complicated and complex systems requires addressing the behaviour of their interactions;
  • Cybersecurity risks affect all aspects of autonomous system safety; and
  • A full picture of system safety is needed to achieve a balance of operations, reliability and other associated disciplines.

BELAZ and ZYFRA enhance mine automation and AI ties

Automation, Communications in mining, Equipment maintenance, IoT, Mining equipment, Mining maintenance, Mining safety, Mining services, Mining software, , , , , , , , ,

Equipment manufacturer, BELAZ and ZYFRA, a company which specialises in industry digitalisation, have agreed to jointly develop “robotisation technologies” for the mining industry and set up a research centre at BELAZ’s facilities for innovation in the fields of artificial intelligence and autonomous transport.

The strategic partnership agreement was signed on July 10 at the Innoprom-2019 International exhibition, in Yekaterinburg, Russia, by Petr Parkhomchik, CEO of BELAZ-HOLDING, and Igor Bogachev, CEO of ZYFRA.

“The main goal of our partnership is to understand better the current and future digital needs of the mining industry and to offer vehicles that fully meet these needs so that customers do not have to waste resources and time upgrading them on their own,” Parkhomchik said. “Identifying these needs will be the object of our joint research activities with ZYFRA and all our future projects will be based on these studies.”

The companies are already taking their first steps together in the areas highlighted in the agreement. For example, VIST Group, a subsidiary of ZYFRA which develops solutions for the mining industry, and BELAZ have launched production of robotised dump trucks.  The vehicles are being successfully used, in particular, in open pits operated by SUEK, according to ZYFRA.

“Experience shows that thanks to accurate tracking of the geotechnology parameters, fully-autonomous and remotely-controlled equipment improves transport efficiency by 20%, while removing drivers from hazardous work zones,” ZYFRA added. “The company expects the solution will be highly demanded by the markets of Sub-Saharian Africa, Chile, Peru and India.”

The collaboration between BelAZ and ZYFRA will have a focus on AI-based technologies, with the companies planning to conduct joint studies of customer needs and an analysis of the global market for digital AI-based products in the mining industry. This will act as a foundation for creating and improving their own developments in this field.

Immediate plans include working on a predictive analytics system for quarry equipment to help predict breakdowns by analysing historical data and carry out predictive maintenance, ZYFRA said. “In parallel, the two companies have mapped out joint steps in the development of industrial safety solutions. In particular, they are planning to test a driver fatigue tracking system using computer vision technologies.”

The companies also plan to develop an environment scanning system for autonomous dump trucks already equipped with artificial intelligence. The system will be able to not only to perceive and react to objects located around the dump truck, but also build a 3D model of the rock mass to be loaded, determine its sequence of actions and correlate its movements with the dump truck’s position.

Bogachev said: “With such a powerful mining technology business unit as VIST Group, ZYFRA is seeking to work closely with the global leaders in the production of quarry equipment.

“I’m convinced that this combination of competences will benefit all parties. For us, it will mean a stronger presence on the global market, a deepening of our expertise and the opportunity to create products equipped with the most advanced technologies, while the mining companies will be able to order their equipment from the plant with their chosen digital features ready installed.”

The agreement includes partnership in the promotion and commercialisation of digital technologies for mining companies and joint training of personnel for the implementation of digitalisation projects, according to ZYFRA.

OZ Minerals Explorer Challenge winners crowned

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OZ Minerals has awarded multiple prizes as part of the online crowdsourcing Explorer Challenge, organised in partnership with energy and resources open innovation platform Unearthed.

The submissions for the crowdsourcing competition to find new exploration targets at the Mount Woods tenements of the Prominent Hill copper-gold mine (pictured), in South Australia, ranged from cutting edge machine learning to advanced physical modelling, with OZ Minerals making more than six terabytes of public and private exploration data available to competitors.

The three month long competition concluded on May 31, 2019, having seen over 1,000 global participants from 62 countries register for the chance to not only win a A$1 million ($701,156) prize pool, but also have its concepts tested in real life, with the top targets scheduled to be drilled by the end of 2019.

First prize (A$500,000) went to Team Guru, a team made up of Michael Rodda (data scientist), Jesse Ober (environmental scientist) and Glen Willis (process engineering) for an approach that included interpretable machine learning models for mineral exploration using geochemistry, geophysics and surface geology.

Second prize (A$200,000) went to DeepSightX, a team made up of Dong Gong, Javen Qinfeng Shi, Zifeng Wu, Hao Zhang, Ehsan Abbasnejad, Lingqiao Liu, Anton van den Hengel, Karl Hornlund, and John Alexander Anderson. This team exploited multi-disciplinary skills at the intersection of artificial intelligence and geoscience, leveraging this to generate an artificial intelligence model to provide promising exploration targets in the Prominent Hill Region (PHR) supported by best practice geoscience.

Third prize (A$100,000) went to Hugh Sanderson, Derek Carter and Chris Green from team Cyency. Cyency has a strong data science and geoscience background, with Sanderson practising “deep learning” for several years, Carter being involved with the technical and software side of mining for over 10 years, and Green being an experienced geologist. The team said: “With so much data, it was difficult to know where to start, so we started with what we knew – the results from the Data Science Stream. We had a set of models that we knew were pretty good at predicting mineralisation across Australia, so we ran them over the tenement…we applied several data science techniques to estimate a set of candidate points, and then selected the 10 best of these.”

The Student Team prize of A$50,000 went to deCODES’ Christopher Leslie, Matthew Cracknell, Angela Escolme, Shawn Hood, and Ayesha Ahmed. A team of early career researchers from CODES, University of Tasmania, its approach was driven by considering an iron oxide copper gold (IOCG) metallogenic model, and then “striving to produce digital proxies for all aspects of that model. Our prospectivity layers were created using a mix of manual and traditional data handling methods as well as basic machine learning approaches”.

The Genius prize (A$25,000) went to Team OreFox’s Warwick Anderson, Sheree Burdinat, Kudzai Dube, Amy Leask, Alan Ryou Pearse, Ashleigh Smyth, and Nick Josephs. The brainchild of two exploration geologists, Anderson and Burdinat, OreFox has built up a team of experts with backgrounds in geophysics, data science, statistics, geology and prospecting to tackle the Explorer Challenge, using its proprietary artificial intelligence systems to analyse the data supplied by OZ Minerals as well as open source data obtained through Geoscience Australia and the SARIG database.

The Insights prize (A$25,000) was awarded to Avant Data Solutions, a multidisciplinary team consisting of data science and programming, and geological domain expertise. The team took a heavily data driven approach with verification and interpretation using geology, with the challenge tackled, first, by analysing and exploring the data in detail and finding what data might be overlooked.

The Data Hound and Fusion Prizes (both A$25,000) went to Team Phar Lap and SRK Consulting, respectively.
Team Phar Lap consists of a mathematician, a physicist, a German trained geologist and ecologist, a pilot, and a US trained geologist, offering a latticework of geosciences and data science. The consortium used a mixed approach between geological interpretation and data crunching with a strong focus on controlled learning.

SRK’s team was made up qualified structural geologists across offices in Perth, Melbourne, Toronto and Vancouver, with “the approach including the re-interpretation and/or value-add of the provided and available datasets followed by a multi-pronged and integrated targeting approach applying data-driven machine learning (based on a balanced random forest algorithm) and weights of evidence to guide a set of knowledge-driven mineral systems informed fuzzy inference solutions”, Unearthed said.

OZ Minerals Chief Executive Officer, Andrew Cole, said: “The innovators who participated in the Explorer Challenge have provided approaches to mineral exploration that we never would have imagined internally, including ways to fuse datasets together, combining multiple layers of information, and making predictions based on the extensive datasets.

“Reviewing the diverse range of solutions that have come back from this process has been truly remarkable.”
Unearthed Industry Lead – Crowdsourcing, Holly Bridgwater, previously worked for a decade as a geologist in resource exploration and definition. She believes that crowdsourcing will transform the lengthy and intensive exploration process.

“We are extremely excited by the incredible range of solutions submitted by these pioneers that can generate high quality exploration targets in an efficient way,” Bridgwater said.

“Many industry professionals and mining companies are beginning to realise that their true competitive advantage in exploration is speed, not necessarily data or technological intellectual property. I think that the ability that the crowd gives you to generate new ideas, develop solutions, and automate processes, is something that can make a big difference and provide that competitive advantage.”