Tag Archives: predictive maintenance

Progesys to bring Asystom predictive maintenance tech to Canada, Brazil mining sectors

Asystom and Progesys have announced an international partnership whereby Progesys will implement Asystom’s predictive maintenance technology across the mining, metal and oil and gas sectors in Canada and Brazil.

Progesys offers project management consultancy and transition-to-operations services to a vast international market. Its origins stem from the aluminium industry and now delve into energy, hydrocarbons, mining, infrastructure, transportation and more.

“Progesys effectively blend their management capabilities with field technical know-how to lead their clients’ operations to success,” the company said.

The Asystom solution monitors and analyses any rotating machine and helps to predict anomalies, wirelessly and non-intrusively, with a simple installation. Sensors communicate through a LoRa (Long Range network, and data is encrypted end-to-end. Asystom’s sensors monitor not only heat and vibration, but ultrasound too, meaning customers have the earliest possible alert of drift (before any damage has occurred). They run on standard batteries (so no need for additional wiring) for up to 10 years without intervention.

“An additional advantage of Long Range is that, due to its low frequency wireless transmission, the sensors can be positioned up to 1 km away from the LoRA gateway,” the company said. “Data is processed at the Edge on the sensors, and then the information is transferred and stored in the cloud through an encrypted secure network and then accessible through web applications.”

Andre Naccache, Managing Director at Asystom, said: “This a very positive next step in Asystom’s successful continued global expansion. Partnering with Progesys will enable us together to allow clients’ projects to achieve their fullest potential.”

Rami Faour, VP Business Development at Progesys, said: “Customers are looking for flexible solutions to meet the changing needs of their organisations. The Asystom solution allows industry customers to increase their production by up to 25%; this is due to the solution’s capabilities of predicting equipment failure and transmitting all data in real-time to any location of the client’s choosing. The information and alerts are then presented on a comprehensive dashboard, making it easy for our clients to see their data.”

Flexco and Uptake devise new predictive analytics tool for conveyor belts

Flexco says it has introduced a new innovative, real-time monitoring system that harnesses the power of predictive analytics so mining, aggregate, and cement operations can remotely gather critical insights to optimise belt conveyor productivity and heighten operational efficiencies.

Flexco Elevate™ Belt Conveyor Intelligence™ is a wireless platform that transfers data insights to an intuitive cloud-based dashboard via edge technology, allowing remote monitoring of belt cleaners, according to the company. With intelligence that grows over time, this easy-to-use platform is designed to simplify and accelerate belt maintenance using its powerful, data-driven engine, Flexco says.

Chip Winiarski, Vice President of Marketing at Flexco, said: “Flexco has a 110-year history of innovation, working alongside our customers to deliver solutions that are in alignment with their operational goals. We are proud to introduce Flexco Elevate. It’s the first technology solution of its kind and will quickly bring increased efficiencies and improved productivity to our customers’ operations.”

Created in partnership with Uptake, an industrial AI and IoT data science leader, Flexco Elevate reduces the need for on-site inspections and allows operations to access real-time insights remotely from an intuitive dashboard, so users are able to quickly address belt conveyor performance issues and minimise unexpected downtime.

“The insights pipeline begins by installing a state-of-the-art Flexco Elevate i3 Device to the end of each Flexco belt cleaner where cutting-edge analytics are immediately aggregated and processed,” Flexco says. “The information is then wirelessly transferred to the Flexco Elevate Dashboard where operations have immediate, remote access to action-oriented insights and service information for all of the Flexco cleaners throughout the operation.”

Flexco Elevate alters the way operations are run and the solution’s multi-faceted benefits streamline internal processes, according to the company. They include:

  • Increased Productivity: the Elevate solution makes it easy to manage belt cleaner service;
  • ROI from day one: data insights are immediately transferred to the dashboard so operations realise operational efficiency from day one;
  • Improved safety: the Flexco Elevate solution reduces the need for dangerous physical inspections along beltlines;
  • Asset management: digital mapping of installed Flexco cleaners provides visibility of assets for more efficient resource planning;
  • No guesswork: the operations team knows exactly what, when, and where service is needed;
  • Leverages existing assets: the Elevate platform works with existing Flexco belt cleaners – no additional investment in cleaners is required; and
  • Remarkably easy: installation and activation in as little as five minutes for a user-friendly experience.

Boliden Kevitsa collaborating on process plant maintenance

Boliden is a front-runner when it comes to applying technology and innovation to its Europe-based mines, and the company is now leading an industry move in condition monitoring and predictive maintenance in its process plants.

At its Kevitsa copper-nickel mine, 130 km north of the Arctic Circle in Finland, Boliden has been collaborating with the likes of IBM Maximo, OSISoft, SKF and Metso on condition monitoring and predictive maintenance solutions, according to Sami Pelkonen, Maintenance and Engineering Manager at Boliden Kevitsa.

Expansion in the plans

The mine is in the throes of an SEK800 million ($82 million) expansion that will see plant throughput go from 7.8 Mt/y to 9.5 Mt/y. This involves the addition of a new autogenous mill and peripheral equipment (including a new Metso MF series screen), and a new mill building. Commissioning of the new equipment is expected in 2020, with the mine reaching full 9.5 Mt/y capacity in 2021.

With this expansion going on, plant maintenance has moved up the agenda.

Some 80% of process plant maintenance is currently pre-scheduled, with the Kevitsa mine achieving, on average, 93% availability from its equipment, according to Pelkonen, but Boliden Kevitsa is looking to increase these numbers.

Pelkonen told IM late in October that the Kevitsa mine has been looking to acquire “good quality…and useful data to support our daily maintenance operations and procedures” at its plant. This is all part of the company’s plan to increase uptime and cut costs at the operation.

As part of this initiative, it installed the IBM Maximo asset management system in May of this year. At the same time, the operation has been working with the Boliden Mines Technology Department on a wider asset management program.

When it comes to plant reliability, Boliden Kevitsa has enlisted the help of SKF (for condition monitoring of bearings throughout the plant), OSISoft for process data acquisition, and Metso to ensure uptime of mineral processing equipment is maximised and unplanned downtime is reduced.

Partnering for performance

The partnership with Metso dates back to before the mine was acquired by Boliden in 2016, but in recent years the two have collaborated on crusher and mill uptime projects, with the OEM supplying mill liners and wear parts that can be switched out quickly and cost effectively. The two firms have also been in constant communication about accessing and analysing valuable process plant data during the last three years.

When the mine acquired a new MF screen from Metso in May (pictured), it decided now was the time to trial the new Metso Metrics predictive maintenance platform in this part of the flowsheet.

Pelkonen explained: “After the increase in production (to 9.5 Mt/y), the front end will be even more critical for us, so we have to be aware if any failures are developing in our front end; especially in our screen.”

The remote location of Kevitsa, situated some 40 km by road from Sodankylä, is also behind the need for this type of condition monitoring and predictive maintenance.

“If something happens like we have an equipment failure, it takes around one hour for our employees to get to the mine,” he said. “Condition monitoring helps us address the need to get resources to site in the correct time.”

The Metso Metrics test paid off almost instantly, when, soon after installation, the company noticed there was something wrong with the running speed of the screen.

“The indication we received from Metso Metrics helped us map out that there were two broken V belts. We were able to cut the downtime to a minimum thanks to the information coming from Metrics,” Pelkonen explained.

Sami Pelkonen was speaking to IM as part of an upcoming Insight Interview with experts from Boliden Kevitsa and Metso that will be published in early-2020

BUMA to tap into tech startup environment with Plug and Play platform

PT Bukit Makmur Mandiri Utama (BUMA), one of Indonesia’s top mining contractors, has partnered with Plug and Play’s global open innovation platform to engage with startups focusing on predictive maintenance, safety and health technology.

Ronald Sutardja, BUMA CEO, said the company was looking to engage with such companies to build “breaking ground technologies” in mining services.

“Technology has always been part of our DNA and we’re striving to be the most technologically advanced company in the business,” he said.

BUMA, currently the second largest independent coal mining contractor in Indonesia according to parent company PT Delta Dunia Makmur Tbk. It carries out a comprehensive scope of work from overburden removal, coal mining, coal hauling as well as reclamation and land rehabilitation.

Plug and Play said: “Having technology in place is not merely about improving worker productivity or acquiring more accurate data, but it is also about improving safety conditions for BUMA’s employees and improving employee’s health.”

Eko Prihadi, Director at BUMA, recently said at Plug and Play’s APAC Summit in Singapore in May 2019: “Safety is one of our biggest priorities in BUMA. As we currently employ more than 13,000 people, we are continuously on the lookout for the latest technologies to improve the well-being and working conditions of our employees.

“As a highly progressive company, innovation is the key to our operations. This partnership will give us access to the global startup ecosystem and allow us to work with the latest cutting-edge technologies to develop innovative business strategies in key focus areas.”

Plug and Play’s Supply Chain platform is, according to the company, the world’s leading innovation consortium with strategic locations in Silicon Valley, Hamburg, Shanghai, and Singapore. Focusing on key areas of relevance, Plug and Play will identify and connect its partners with the latest technologies that will accelerate their innovation efforts.

Wesley Harjono, Managing Partner of GK-Plug and Play Indonesia, said: “We are very excited to have BUMA join us as our newest supply chain partner. Since the inception of the platform out of Silicon Valley in 2016, the vertical has amassed more than 35 industry-leading corporate partners including DHL, ExxonMobil, and ArcelorMittal. Partnering with BUMA will help us drive technological advancements in key areas such as safety, wearables, and IoT in the mining industry.”

Future-proofing mineral processing plants

As minerals processing, digital plants and effective plant operations become more important for mining companies, Australia’s largest mining event is set to examine the challenges of processing plants of the future.

Finding intelligent solutions, future-proofing grinding circuits and embracing the opportunities of digitisation will be discussed at the International Mining and Resources Conference (IMARC) in Melbourne next month (October 29-31).

Ahead of the conference, Sandvik Lifecycle Development Manager, Simon Adams; CRC ORE Chief Executive and Managing Director, Ben Adair; and Weir Minerals Global HPGR Product Specialist, Bjorn Dierx, discussed the issues in a special IMARC webinar.

All agreed mining companies faced increased challenges as ore stocks depleted, forcing them to move to more remote locations and dig deeper in a bid to maintain recovery rates of past years.

Dierx, who will deliver an IMARC presentation on dry air classification technology to remove the need for water, said: “Our customers are under immense pressure to reduce energy consumption, use less water and reduce carbon emissions.

“Overall, as commodities are depleting, companies are making large investments in new plants to dig deeper, crush more ore and at remote locations with limited access to power and water to achieve the same recovery rates as the past 20 years.”

He said about 3% of global energy consumption was attributed to crushing rock so greater efficiencies in comminution would make a big contribution to reduced emissions.

For Adair, efficiencies are available now in existing operations.

“It’s important to optimise and run your equipment to the best of its ability,” he said. “Most sites I visit that’s simply not the case. We are a little bit delusional if we think we are there at the moment in a digital sense in optimising various grinding circuits.”

He agreed limited access to water was a critical element.

“It’s interesting water was mentioned. That is one of the major challenges for the industry. It simply won’t have access to potable water and it will have to head rapidly to a closed-loop situation otherwise the costs will be extraordinarily prohibitive,” he said.

“Most of our work is done in the sorting space. . . It’s patently ridiculous and it has been for the past 15 to 20 years that we mine something and stick it through various expensive process plants when in fact 99% of it has no value whatsoever.

“If you are looking at the mine of the future, it is going to be about exploiting heterogeneity at the mine face as opposed to deliberately destroying heterogeneity and looking for homogenous feeds for downstream processing plants.”

The digital transformation at the plant and processing level offered opportunities for miners, with Adams saying the ability to collect and analyse data was crucial.

“If you can have digitisation and automation that moves towards cognitive behaviour, once you get those algorithms down you can have far more efficient plants operating through that process,” he said.

“We have to turn data into knowledge; looking at power consumption and efficiencies and getting to the cognitive stage where we can foresee failures or predicted failures and we can capture them early and shut down in an organised fashion.”

Dierx said digital transformation presented a big opportunity for the industry to attract new people from traditional software programmers and those in the gaming industry to work in the mining industry.

“The big iron ore miners, if those autonomous devices need to be switched off, they use Xbox controllers to correct them. That’s good news for children of today,” he said.

“From an education perspective, there is still some work to be done. Universities need restructuring to ensure we not only educate traditional operators, metallurgists and process engineers but ensure that understanding algorithms and working with digital tools become standard practice.”

IMARC, developed in collaboration with its founding partners the Victorian State Government of Australia, Austmine, AusIMM and Mines and Money, is where global mining leaders connect with technology, finance and the future. For more information, please visit https://imarcmelbourne.com/

International Mining is a media sponsor of the IMARC event

Ava Risk Group, Mining3 launch Aura IQ conveyor monitoring solution

Ava Risk Group and Mining3 say they are ready for the global launch of the Aura IQ conveyor health monitoring solution following surface and sub-surface testing with some of the world’s largest mining houses and bulk material handling facilities.

With conveyors underpinning efficiency, and ultimately profitability in bulk handling operations globally, maintenance has traditionally been a real problem.

“Conventional methods of advanced conveyor failure detection is often unreliable, subjective, time-consuming and labour intensive, but that is all about to change,” Ava and Mining3 said.

Aura IQ uses real-time data to optimise production and on-site performance, enhance occupational health, hygiene and safety management, and introduce new predictive maintenance and support capabilities to asset management, they say.

With test work in the bag, Aura IQ is now available for sale globally.

The companies said: “Aura IQ’s award winning technology harnesses the power of Ava Risk Group’s fibre optic detection and sensing platform (FFT TM Aura Ai-2), combined with Mining3’s advanced signal processing algorithms, predictive analytics, and identification tools to acoustically monitor and assess conveyor health via the cloud-based analysis, reporting and alerts.

“Providing deeper insights to maintenance technicians, site personnel, regional operational hubs and global headquarters, conveyors are automatically connected to the cloud via an Industrial Grade Wireless Internet of Things Gateway, enabling daily asset reliability reports from every conveyor, at every site around the world.”

By transmitting a series of short, laser pulses along a single fibre optic cable retrofitted along the length of a conveyor, acoustic disturbances from the conveyor system cause microscopic changes in the backscattered laser light that is then categorised into known parameters, the two companies explained.

Data is then simultaneously gathered from every metre of the conveyor and processed by Aura IQ to pre-emptively alert operators, either on or off-site (in operational hubs or control rooms), to potential failures before they happen.

Andrew Hames, Head of Innovation, Extractives and Energy at the Ava Risk Group, said: “This is a game changing solution which will optimise conveyor performance and create substantial cost savings for operators.

“A typical conveyor can have up to 7,000 bearings per kilometre, which means 7,000 potential points of failure. Aura IQ can monitor the condition of every conveyor roller – eliminating the need to ‘walk the belt’ and allowing a controlled and scheduled plan of roller maintenance and replacement to be put in place.

“With Aura IQ, costly delays from roller failure are a thing of the past, while less manual involvement reduces health and safety risks. Taking a formalised and proactive approach to asset health monitoring means data can also be used to optimise maintenance strategies – reducing reliance on costly manual inspections and demonstrating ongoing compliance with operational standards.”

Wenco makes Latin America expansion plans with TecWise partnership

Wenco International Mining Systems has announced a new partnership with TecWise Sistemas de Automação, a provider of technology and communications systems to the Latin America mining industry.

This new agreement makes TecWise the exclusive distributor of Wenco solutions in Brazil, paving the way for customers in the largest Latin American country to leverage “Wenco’s open and interoperable approach to mining technology”, Wenco said.

Wenco’s data solutions are designed to boost productivity, decrease operating costs, extend equipment life, and give mining companies actionable insights into their operations. Its Mine Performance Suite consists of systems for fleet management, high-precision machine guidance, predictive maintenance, collision avoidance, and mining business intelligence.

Unlike other solution providers, Wenco, a Hitachi Group Company since 2009, has designed its systems with an “open systems philosophy” that, it says, “empowers customers to freely integrate systems to support their unique business processes, data requirements, and reporting needs”.

“TecWise and Wenco formed this partnership based on their shared approach to delivering customer-focused solutions to the mining industry,” Wenco said. Founded in 1997, TecWise works closely with customers to design, deploy, and support fit-for-purpose, performance-driven technology that improves efficiency, safety, and productivity.

“The company’s history of strong support throughout Brazil made it a natural partner for Wenco as it expands through Latin America,” Wenco said.

Andrew Pyne, President and CEO of Wenco, said: “We’re excited to enter the strategic Brazilian mine market in partnership with TecWise. In line with our focus on growing our presence in Latin America, Brazil is a key strategic priority for Wenco, particularly as we collaborate with Hitachi Construction Machinery on their Solution Linkage for Mining platform.

“We have planned this collaborative entry into the Brazilian market for some time and we took our time to identify the right partner, which we found in TecWise. They will ensure our customers have knowledgeable, on-the-ground local support for Wenco solutions for the long haul.”

TecWise Business Director and CEO, Omar Garzedin, said: “The level of flexibility and openness of the Wenco solutions, the philosophy of interoperable standards – this is what initially caught our eye and made Wenco stand out among data solutions providers for the mining industry.

“Over the years, a common challenge for our mining clients has been the ‘closed stack’ approach of many suppliers – the difficulty in controlling and using their own operational data in the manner that they prefer.

“When we shared the Wenco philosophy – the ability to react to a client need in an agile manner, combined with a global track record – we all clearly saw new ways of addressing long-standing challenges in an innovative, scalable, and cost-effective manner.”

TecWise is in discussions with mines throughout Brazil to offer new ways to solve known problems, while extending new capabilities and options through Wenco’s open standard approach to mining technology, Wenco said.

Dingo improves Trakka predictive maintenance capabilities with AI

Dingo says its new Trakka Predictive Analytics solution uses artificial intelligence and machine learning to predict impending equipment failures with confidence, allowing customers to proactively perform corrective maintenance actions to minimise downtime and optimise asset life.

The release comes around five months since the company laid the groundwork for the new solution with an announcement that it would introduce practical machine learning models built using real customer data and targeted at specific industry problems from January.

The new Trakka solution includes a series of sophisticated predictive analytics models to provide anomaly detection and failure prediction for asset intensive industries, the company said. These models are built by uniting failure data from actual equipment, “Dingo’s industry expertise and data science to address common component-specific failure modes, such as final drive gear teeth wear”.

Powered by a proprietary machine-learning library, the Trakka Predictive Analytics solution can, Dingo says, predict the time until asset/component failure with a high degree of accuracy. The company said its customers will reap the benefits of these remaining useful life (RUL) models (pictured) as they:

  • Reduce unexpected failures and downtime;
  • Reduce repair cost as scheduling is optimised;
  • Reduce loss of wasted potential in capital;
  • Reduce unnecessary maintenance activities;
  • Reduce personnel and process risk by creating a safer and more controlled environment;
  • Improve component life by acting earlier;
  • Improve confidence in planning component replacements;
  • Improve equipment availability and reliability;
  • Improve budgeting and the bottom line, and;
  • Improve business related processes such as procurement, logistics and management.

Dingo said: “Before any predictions can be made, Dingo’s domain experts and data science team work with a customer’s historical failure and condition monitoring data to deploy or adapt existing models or create new machine learning models to correctly identify failures within the customer’s fleet.

“This process typically involves data collecting, cleansing and validation to ensure model outputs are as accurate as possible. The transition to online predictive analytics is complete once the data ingestion pipeline is ready and the models are fully trained and tested.”

The predictive models are designed with scalability in mind, Dingo said, meaning they can be easily re-trained to work with a broad range of asset and failure mode problems experienced by real mining operations, making them highly reusable.

“The models are continuously optimised through ongoing validation and the input of new data and equipment performance information,” Dingo said.

And, the platform connects a broad range of systems and software to provide data surrounding asset health, including enterprise resource planning & enterprise asset management systems, computerised maintenance management systems, fleet management systems and all forms of condition monitoring data, including oil analysis, visual inspections, sensor data, vibration and thermography.

Schenck Process filling screen performance data gaps with sensors

Schenck Process says performance data provided by extra sensors fitted to a prototype vibrating screen is substantially improving the understanding of operation of the equipment.

The data is also giving indicators about the overall performance of the processing cycle, according to the company.

Designed and developed in Australia by Schenck Process, the prototype screen is undergoing site trials, but the company already believes the new screen has the potential to change the way vibrating screens are developed and operated.

The standard condition monitoring system comprises two sensor nodes including six degrees of freedom MEMS accelerometers, a high-resolution accelerometer and a temperature probe. On the prototype screen, four additional sensors have been fitted, one on each corner.

Schenck Process Senior R&D Engineer, Doug Teyhan, said: “The measurement regime for the additional sensors includes spring amplitude and mean compression, allowing the estimation of tonnage and load bias (to determine if the feed is presented square to the screen or favouring a side) and the determination of spring operating characteristics and cumulative fatigue damage.

“We are also looking into the development of a predictive failure program to improve overall productivity and efficiency and significantly reduce the possibility of unplanned downtime.”

Historically, failure prediction has been determined by running components to the point of failure and assessing a mean time to this point based on a known operating history.

“The data generated by the prototype screen is utilised to estimate the operating stress of the screen at the most aggressive fatigue areas and assessing the cumulative damage of those areas based on the measurement of non-ideal operating characteristics,” Schenck Process said.

Using a Cumulative Damage System, which counts machine cycles and trend characteristics that have the potential to adversely affect vital component life expectation, the plan is to make the machine monitoring system a lead measure in predicting the potential for component failure, Schenck Process said.

“The expanded monitoring system will also provide input into machine development of the next generation of vibrating screens by filling in the unknowns in the design process with real-time field data,” the company said.

According to Teyhan, the benefits for the customer – including increased availability and improved screen performance – are substantial and have the potential to initiate improvements in the processing cycle.

“And, from a screen operation point of view, the additional data is bringing to light characteristics not previously known. It is highlighting transient feed characteristics – not visible using traditional condition monitoring techniques – that impact the loading of the screen and affect machine life expectation,” he said.

“We also believe there are potential industry-wide benefits, through new design parameters and possible changes to machine construction techniques and materials,” he added.

To optimise the greater range and scope of data the screen is generating, the company is collaboratively investigating and assessing other performance variables, it said. The potential is for control of the variability in the feed rate, more consistent performance and improved overall efficiency of the cycle.

Newtrax AI helps out Agnico Eagles’s Goldex mine maintenance team

Newtrax Technologies says it has applied machine-learning algorithms to help Agnico Eagle Mines’ Goldex mine predict mobile equipment maintenance issues up to two weeks in advance.

With the two companies already having an existing relationship at the mine, in Quebec, Canada, Newtrax was approached in the fall to discuss the data Agnico had collected from sensors over the past six years. This amounted to 10 billion data points, according to Newtrax.

“This data was exactly what was needed to apply machine-learning algorithms in order to predict mobile equipment maintenance issues at least two weeks before they were supposed to happen,” Newtrax said.

Daniel Pinard, Team Lead, Special Projects with Agnico Eagle, said this predictive Newtrax AI solution allowed the company to intervene before incurring serious problems that could potentially break vehicle engines.

“Through the use of machine-learning algorithms with Newtrax, we were recently able to analyse an engine that had a potential problem and we saved it from failing. This helped Goldex mine avoid serious damage on that engine which saved them C$85,000 ($63,610).”

The Newtrax AI solution is unique in three ways, according to Michel Dubois, VP QA & Artificial Intelligence at Newtrax, “first, Newtrax has years of unique data that is extremely well suited for machine learning (ML)”.

This creates a source of training data for ML that is unique in the world, with the data growing every time a mining company decides to join in, he said.

“Second, we have a unique AI team who knows how to generate actionable results using existing AI algorithms. And, third, we have a unique approach where our AI specialists go underground and focus on quick wins, and they leverage those existing algorithms to solve high-value problems.”

This is the first ever applied case study for ML in the underground hard-rock mining industry with a defined return on investment, according to Newtrax.

Newtrax said it worked with artificial intelligence and ML researchers such as IVADO to apply existing algorithms to the data collected in mine sites.