Analysis and Algorithms
Core to business – and one of the many factors driving the Techenomics foray into the Newman area – is analysis of used oil and other fluids, such as coolants, grease, fuel, and other fluids and lubricants, under a range of conditions.
Techenomics’ condition testing extends beyond standard OEM recommendations, and takes into consideration factors of individual site conditions, the nature of material being handled and the machine’s use.
Coal has been Techenomics’ bread and butter and is at the centre of its business in the Hunter and throughout Indonesia. In Bangkok the business turned its attention to condition testing in a gold mining setting, and then in Mongolia it was a copper-gold mining location.
Now in the Pilbara, iron ore operational conditions are the focus of attention.
“The company’s distribution of liquid tungsten, which is a disulphide nano oil additive used in thermodynamically sophisticated friction reduction, also made the Pilbara lab a strong proposition,” Chris said.
“With the COVID-19 pandemic, came the need to sharpen the business’ turnaround and service delivery, so a Newman laboratory became a priority, and the benefits were immediate.”

Chris said like the company’s other labs, the Newman operation would use intuitive Blue Ocean Software to store, manage and analyse the large volumes of data being processed across its network.
He said a sophisticated library of algorithms and conditional variables, which have been developed and built into the platform, enables acutely accurate conditional testing
and analysis of oils and other fluids.
This platform will make servicing the Pilbara, from the newly-launched Newman Laboratory, more effective and efficient, especially in a post-COVID setting.
“This data is housed in sets assigned to each client, which means customers can only access their own specific data sets,” he said.
“This is a platform which can also be fully integrated with a client’s IT interface – meaning unabated access to vital data as well as trends, predictions, and anomalies.
“Once collected, the platform stores the data, creates a history and sets trends or predictions – and provides a small but growing set of analytics.
“It is consistent across the whole cloudbased platform – so, wherever the customer, site or the machinery on which the condition testing was done, the data is available on
exactly the same cloud-based platform.
At the laboratory in Newman, Kiky Millar, a recent arrival in the mining town from another large mining service centre in Balikpapan, is lab manager.
Kiky relocated with her husband, who has work in the Pilbara with BHP, and two young children, and brings more than six years previous laboratory experience as well as
skills obtained while completing a Bachelor Degree in Chemistry as well as a Masters Degree in Environmental Science.
Assisting Kiki in the new laboratory will be a female chemical engineer, also from Indonesia, as well with Keshini Lokhun, a young science graduate specialising in
chemistry, who is also helping Techenomics establish a presence in parts of Africa with her French language skills.
Kiki says women are playing an increasing role in the male-dominated mining industry, including in places like Newman, and particularly in technical and scientific areas.
She says that women are now much better accepted and there are more opportunities.

Testing Times
Chris likens the conditional oil and fluid testing and analysis to human pathology testing: just like a pathologist will look beyond the blood, Techenomics analysts look well beyond the oil – and for much the same reasons: to ensure a comprehensive analysis of wear and condition is undertaken.
The testing procedure begins with a sample of oil or fluid being taken.
The samples, usually between 80ml and 100ml, are taken in a standard manner, and when oil is hot. It needs to be indicative of the compartments and be consistent – engine to engine, compartment to compartment, gearbox to gearbox.
Samples are then labelled according to what it is, what the hours are, and any other details, and then the sample kit is dispatched to our lab – either by couriers, hand collected, or post office bag.

The new laboratory in Newman will heavily reduce testing, delivery and analysis time for any samples collected in Western Australia – especially from within the Pilbara region. The reduced time taken to transport samples to the lab means analysis can be completed sooner, customers are provided with the results and action can be taken sooner to resolve issues or prevent further wear or damage to components.
It’s at the lab that the AI magic happens.
Testing is undertaken on a scheduled basis.
While this is usually consistent for most mobile equipment, there are variables which need to be taken into consideration.
For mobile equipment, Chris maintains there is a vast difference between a coal and a hard rock mine, for instance.

The specific gravity of the material the trucks are carrying – and the diggers are digging – changes, so the volumes change. Simply put: 200t of iron ore are much smaller in volume than 200t of coal.
Again, this presented another opportunity in the opening of the Newman laboratory.
Considering the many variables is vital in identifying potential issues, preventing unscheduled stops and emergency
breakdowns and ultimately saving time and money.
“A piece of machinery – for instance a dump truck – might have an engine worth a couple of million dollars so the capital value of that equipment is very high, and the more sophisticated miners want to make sure they achieve the maximum life for the components fluid and everything is running efficiently and in a healthy manner,” Chris said.
“The fluid analysis provides data to be collected and then analysed, to enable advice on the health of the equipment to be provided, as well as what may be necessary to ensure long life and make sure no breakdowns occur.
“While some customers seek only the data, so they can make their own decisions – either with or without consideration of OEM/standard recommendations for oil and fluid replacement – others request a full data collect and analysis with provision of reports and recommendations of a way forward.
“Both requirements rely on a fast-aspossible turnaround – especially when an issue has been identified and a reliable efficient diagnosis means limiting lost time and money.
“We tend to have locations that are close to activities so the time in which we can return the information is reduced.
“We saw through COVID how clients throughout the Pilbara would benefit from the lab in Newman and it now enables us to provide a faster and more efficient service to our customers in that area.”
The power of platforms such as Blue Ocean means prevention, risk mitigation, downtime reduction and maintenance can
be assessed and planned for using reliable, site and condition-specific data.
It also heightens the accuracy with which everything from a particle in oil to the risk of, for example, bearing wear can be Identified – and vitally, allow for the consideration of the possible relationship between the two
different findings.
About 70% of oil tested is usually of a fine standard. But that remaining 30% is such that it either needs attention or it is an absolutely urgent issue which required immediate intervention.
“The most common element you’re going to find is iron,” Chris said.
“But that, as well as traces the likes of copper, chrome and aluminium, is generally something which flags issues around wear.
“The presence of that can also lead to considering the quality of the lubricant being used in a compartment.
“If viscosity is low due to a fuel leak or a coolant leak oil will lack the capacity to lubricate. When breakdown can start to occur, there will be wear earlier on and this points to the deterioration of a compartment.
“By this stage, failure has started so it is then a case of how you manage the remaining life of that compartment.”
It all starts with the oil – if that is okay then the source of the failure needs to be found and signs could be bearings, or rings in the gearbox or big lumps of iron which have been knocked off the teeth. It is a reliable indicator of the health of the overall machine. If a compartment is running too hot, the oil degrades, and oxidation is evident – this indicates too much friction.

Skillsets
Chris credits the high level of skill and extensive experience of the team with being able to leverage good outcomes for clients by working with the data and using the algorithm-based platform.
Techenomics is a team which works as a global network as opposed to silos. While each member will employ the years of experience, they have to analyse what they see, and what the data tells them, they base solutions on a bank of knowledge shared across laboratories – and continents!
It’s where tech and AI meets and is complemented by what humanness brings to the table.
“We can provide a range of data for everything from a dump truck engine to a transformer in a power station to a rail locomotive,” Chris said.
“Some customers rely on us to advise with a report on what to do with oil and what to do with compartment; whereas some others just want the data streamed into their own platform or software.
“We have tried to structure the business so we can make available across the group any skill gained at a particular site or location.
We share our knowledge and the skills we learn and we also share our challenges – we avoid working as a silo – and the collective skills and abilities benefits everyone.”
It’s a team that makes the technologyartificial intelligence-data-human mix work well.
“We have a lot of Masters, the odd PhD across the locations … we have a few smart people here – and we have the experience of those people with hands on skills, on the ground with an understanding of the locations and conditions our customers are working in,” Chris said.
The blend of youth and age bodes well for the business, especially given the need to navigate successfully through the digital space.
And as for digital, Chris said the platform being cloud based would enable growth and expansion in the organisational sense, but also facilitate continued and growing access for customers as well as growth in the as
collective and sets of data.
Predictably, Artificial Intelligence will underpin Techeconomics’ intention to remain at the forefront of the industry, finding technology-driven and innovative solutions.
It is also what will enable an investment in research and development – as well as expansion into new products, services and offerings.
Currently underway, and linking existing concepts with new technology, is development of remote, real time condition
monitoring.
This brings the magnetic plugs which collect, check and/or filter iron, together with an innovative, remote, and real-time system of monitoring and analysis.
This will provide a tailored system via which issues and risks can be identified – remotely and in real time – and intervention can take place in early stages.
Even in maintenance, this technology would enable a more cost and downtime effective use of oil, while real-time monitoring of compartments and fluids are taking place in the background.
Chris said this will be most beneficial in those cases where oil might be in good condition but it is changed based on an OEM recommendation standard.
This will mean that if the oil is fine, a recommendation on when replacement should be carried out is given based on
specific conditions as opposed to a set number of operational hours or kilometres.

Oil’s Well That Ends Well
Whether in Newman or any of the other laboratories across the Techenomics network, the technology being used to test and analyse oils and other fluids – as well as the strategic location of where that testing can be undertaken – is all aimed at minimising downtime and maintenance costs.
Further, each location and set of associated conditions can be accounted for in testing and analysis.
“This is why we call it condition monitoring,” Chris said.
“We consider the condition of the oil and other fluids, not just how many hours or kilometres have been done.”
This level of analysis – which is already underway to an extent – means tangible savings in time, money and risk.
Most importantly, it is the prevention of unscheduled breakdowns that this will have potentially the greatest impact upon.
This and the careful location of laboratories in locations such as Newman to ensure a more timely and efficient turnaround; along with the liquid tungsten developments and other research, sets organisations such as Techenomics on a trajectory which might have been impossible to plan for 15 years ago when their investment in Blue Ocean software was first made.
But in this climate, and with AI technology now “marching down the road”, as Chris puts it, nothing is off the table.
Not if it means a tailored and condition-specific offering for customers around the world.

SOURCE
Techeconomics Pilbara
Keshini Lokhun
E Keshini@techenomics.com
Kiky Millar
E Kiki.Millar@techenomics.com

The post Intelligent Analysis in the Pilbara appeared first on The Australian Mining Review.

With 60pc of time taken up in collecting data, and a lack of consistency in the data even before it could be analysed and diagnosed, he set his team to the task of eliminating the need for manual field monitors.

The result is the development of a sensor system that is continually on-line, allowing the time previously used in data collection to be applied to analysis and monitoring of the condition of machinery in a plant.

The Tui system allows the vibration monitoring to be completely controlled by external experts, who can focus solely on the function of the individual machines without the distraction of the plant operations.

The worldwide vibration monitoring service, based in Queensland, now services a range of major clients including servicing BOC Gases Australia in eight different countries.

The low-cost system has many superior features in comparison to both manual collection and other sensor systems.

Low capital expenditure

Tui’s business model allows companies to gain approval for the project more easily than the traditional model and proceed with online conditioning monitoring without delays.

All hardware except the cabling is included in the monthly service fee and is installed and monitored by Tui at no extra expense.

This lowers the cost of the system by tens of thousands of dollars.

“We can offer a vibration monitoring system that is more effective and reliable at a lower cost,” Mr Sulzberger said.

Smooth installation

Tui spent time during the design period ensuring that initial installation was seamless and simple.

“As the system was developed from scratch, we spent time optimizing the installation process and constructing components to enable us to have quick and easy placement,” Mr Sulzbeger said.

Thanks to this initial attention, Tui can place sensors quickly with minimal interruption to the operations.

For a client who orders 180 sensors, at six per machine, the sensors can be installed within five days, in most cases while the machines are operating.

Alternatively, on-site personnel can be trained to install any additional sensors the customer wishes to order at a later date.

Optimal placement

Tui has calculated the optimal number of sensors required that will both provide accurate data and avoid unnecessary expense.

It aims to apply one sensor per radial bearing and one per axial shaft, resulting in, for example, three sensors for an electric motor.

The Tui sensors need only be placed strategically on the weakest axial bearing which helps the clients save money.

“Normally motors will not have much in the way of axial vibration, however, the axial sensor will pick up a range of faults that arise such as coupling issues alignment, bearing preload, unbalance and poor installation practices,” Mr Sulzberger said.

Experts on the job

Staff retention and expertise has always been an issue in the vibration monitoring industry, as often the job is given to entry level staff who then move on quickly.

Staff with the knowledge and competence required to properly collect and analyse data are continually lost, which greatly affects the quality of the data.

Condition monitoring relies on consistency as well as regular and tight intervals at the point of collection.

A manual monitoring system uses at least 60pc of the time to simply collect the data, a process which requires running a hand-held sensor over a selected location on each machine individually, and then returning after an interval to repeat the process exactly.

According to Mr Sulzberger, the accuracy of this system is usually around 40-60pc, a figure that is heavily dependent on the quality of the data collection, as opposed to a 98+0pc accuracy rate with Tui’s online system.

Various literature states 40pc of bearing faults are random, but this has proven not to be true.

Usually, the main problem is the collection interval is not set correctly to capture the failure mode.

The interval of the manual monitoring data collection can vary from two weeks to once a year, making analysis difficult.

The Tui system overcomes this by replacing manual monitoring staff with a sensor that streams data to qualified engineers who assess the information that comes in continually as online data.

The analysis can be done by Tui, the clients condition monitoring team, or jointly.

Pin-pointing the faults

Vibration monitoring is essentially looking for change, often subtle

If the vibration signature starts to change then that is an overt sign of malfunction within its workings.

With online sensors placed at the bearings, the signs of malfunction can be picked up long before the vibrations themselves are noticeable.

The constant stream of data allows incremental changes to be picked up by Tui analysts in real time and the amount of data allows Tui to provide accurate information about what the fault actually is.

It can then notify the client and help it assess and decide on how to proceed regarding maintenance.

Using the information

Tui monitors not just the information provided by the sensors but the sensors themselves, with a monthly audit.

If the sensors are not sending data, then either they are not working, or the machine is turned off.

This can be cross checked with the other sensors in the machine – if all three are not reporting data then the machine is off, otherwise the issue is with the sensor itself.

Identifying machines that are offline for some time can also help the client to ensure that its protocols are being adhered to.

The data can identify machines that have not been turned on for several months.

Many issues arise if a machine is left idle.

Valves can cease to function or in the case of pumps, calcium build-ups can block the volutes – issues which may only be noticed when the machine is required and doesn’t function.

Tui can identify the level of criticality of a fault that arises by the sensor output – if faults arise at more than one sensor then the level is raised higher.

“The smallest of machinery can take out an entire operating process, hence the system was designed to cover a wide range of equipment, not just the high capital machines” Mr. Sulzberger said

Case study

Tui was monitoring a motor which failed very quickly, a condition which it alerted the client to as soon as it was detected, prior to the failure.

The client proceeded to conduct repairs based on its own diagnosis which unfortunately resulted in inaccurate machine repairs.

After the second fail, Tui repeated its diagnosis based on its streamed data and convinced the client to send the parts back to OEM for professional repairs.

Its diagnosis proved correct and the motor was repaired correctly, allowing the machine to provide many more years of service.

Without Tui’s accurate diagnosis based on consistently provided data, the collapsed internal cage of the motor would have created structural damage to the machine and it would have needed to be replaced.

On a $100,000 machine, faults can have disastrous consequences but with the accurate pin-pointing available from Tui’s online sensor analysis, these ramifications can be avoided.

The installation of Tui’s vibration monitoring sensor system is a positive investment, allowing machines to be assessed for faults before they happen.

Tui’s diagnosis and evaluation process will allow repairs to be made in a timely manner, ensuring ongoing operations and massive cost savings.

More information:

Phone: 07 37157800
Email: office@tuiindustries.com

The post Good vibrations appeared first on The Australian Mining Review.

This is now a reality thanks to developments in big data acquisition and modelling, machine learning, prediction and simulation software and a dramatic increase in the accuracy delivered by all these tools.

Welcome to the new era of digital twinning an entire mine.

Typically, mine optimisation was conducted in silos – drilling, blasting, crushing, recovery, milling etc. – looking at individual decision choices and set points within a plant to achieve the best results in that specific centre.

However, by utilising historical and current data from the entire mine value chain, modelling the whole process and then relating it back to the individual geology blocks, it is possible to create a unique, new paradigm for prediction, simulation and optimisation for all mining processes.

Petra has spent many years developing cutting edge digital twin models.

The end goal was to optimise processes such as extraction, comminution and recovery for different geology and this is what their unique MAXTA software delivers – and then some.  Changing geology is responsible for much of the uncertainty in traditional modelling, so Petra (which is Greek for rock) made it their mission to re-write the manual and give engineers and geologists alike, greater detail and accuracy in their predictions and simulations.

How to optimise for geology

The challenge has always been how to optimise for the different geology that comes through each mining process.  Digital twin models are more widely known in a manufacturing context where inputs can be measured and the behaviour of outputs can be defined by physics.

In mining however, inputs are primarily rock, whose behaviour is difficult to measure at any scale.

Indices exist to measure certain characteristics such as blasting and geotechnics, while processing is based upon laboratory scale mineralogical analyses, and pilot scale tests.

But the advent of ‘big data’, cloud computing and machine learning now provides the possibility to learn from historical performance and no longer rely on static models.

It is now possible to use a mine’s actual performance in conjunction with machine learning (actually learning from the data) to determine what that particular model should be.

Historically, explicit models were used, where data is input into a known function and then outputs are derived.

However, outputs from functions are typically prone to errors in the region of 10-40pc because of the uncertainty associated with how rocks behave in different geological conditions.

With machine learning, there are large scale inputs – essentially all relevant data from the whole mining history.

This especially includes geological data such as the block model, drilling, weather and hydrology – all overlaid.

Then, the downstream process performance data is used (outputs from plant, crushers, mills and other value drivers).

Machine learning then takes the inputs, compares them to the various performance outputs and works out the actual function.  It automates the process of defining a function that explains the relationships between inputs and performance outputs in the mine.

This can be done at each point along the value chain.

This is the power of Petra’s MAXTA software platform.

The hard part is the ability to mine this data and work out the attributes of the ore as it arrives at each point of the value chain.

Petra commenced four-and-a-half years of ore tracking research and development with a case study from Telfer mine at IMARC in 2015.

Since then the company has worked with iron ore (haematite and magnetite deposits), copper-gold porphyry, epithermal gold silver and others to map data such as SAG mill throughput and recovery back to the pit – also applying all available big data from the mine to the mill model.

This process has now evolved, and is far more complex, incorporating blended stockpiles, crusher data and conveyor systems.

The end result is the ability to know what ore is arriving at any particular point in the chain.

When drilling, weather and geological data is added, it forms a solid basis for building a whole-of-mine model.

Prediction

Digital twin models feedback historical and current data performance data to predict given outputs from geological block model and other data.

Plant performance and throughput can be put back into the geological model via the block model and be used to derive more accurate scheduling and mine optimisation.

This is the realm of MAXTAGeomet package.

Predictive modelling can be used, for example, to see how geology will play forward with a particular schedule of blocks to be mined.

MAXTAProcess software maps how this will play out over a week, month or year.

Simulation

The digital twin software is well known for its ability to run complex simulations and deliver highly accurate results.

It allows engineers and geologists to ask questions of the model – most often in relation to drilling and blasting.

MAXTADrill &Blast has different drill and blast design options or levers that engineers can utilise to test the effects of variables such as drill patterns, explosive types, blast hole diameters and more.

These can be observed in relation to dig rates, crusher throughput and mill output.

Interesting insights are often uncovered through these simulations such as certain inputs improving the performance of one crusher but impeding the performance of another.

Hence it is possible to run simulations until both are optimised for maximum total output.

Exploration applications

A natural progression for the use of this big data is in brownfield exploration.

MAXTA provides the opportunity to better predict the behaviour of ore in the plant and even the grade, so it can also be a useful tool for exploration modelling along strike or down dip.

As with all forward prediction models, digital twin provides the ability to learn from past data and that is also true for exploration planning.

Reconciliation

Blind testing by users of MAXTAGeomet software has returned month-by-month accuracy of between 0.5-1.9pc accuracy when analysing tonnage of mill throughput.

When this is compared to conventional empirical static testing accuracy levels of between 10-20pc error – the difference in accuracy is staggering.

A predictive model capable of delivering results accurate to less than 2pc is a powerful tool and allows mining companies to reduce uncertainty surrounding their projects and articulate a lower risk opportunity to potential investors, or financiers.

This increases their ability to raise funds in a highly competitive market.

Optimisation

An optional optimisation layer is built in to MAXTA which goes over the top of the machine learning level.

Effectively, the optimisation level asks the model ‘how do you get the best performance for particular geological conditions?’

Primarily used for processing plant use, the same principle is also applied to drill and blast.

Again, through simulation, the model is able to suggest the optimal design based on historical performance.

Education and acceptance

Providing accurate models and simulations in this manner is a big change for a lot of people, according to Penny.

“It is an area we have been working on over the last two years in particular,” Penny said.

“How do you present optimal decision support in a way that engages the majority of engineers and geologists?

“Tech guys already understand the advantages but in order for the benefits to be scalable, there is a need to engage all users.

“We need to make it part of the normal workflow and enable people trust it.

“Engineers and geologists on site, who are required to deliver KPIs need faith in the tool.”

To further enhance user uptake and confidence in MAXTAInterp was added.

This includes machine learning ‘glass boxes’ where users can see the relationships between inputs and how they interact.

Most mine digital twins have between 40 and 80 inputs, so when users are able to literally see what is going on inside the MAXTA models, they are able to ‘sense check’ these interrelationships and ask themselves, ‘Do I believe that this is correct?’

After asking questions and seeing results that agree with their own experience, users can then trust the system in an operational environment and begin to use it to its full potential.

A real-world example of this acceptance and confidence was experienced by PanAust.

MAXTA was used to analyse and model their tailings grade, which is a good proxy for recovery rates.

The modelled data had good agreement with historical data and performance and clearly reproduced areas in which they had experienced difficulty with recovery.

This resulted in engineers and geologists alike, placing a high degree of confidence in the MAXTA models .

Implementation

Implementing the MAXTA on a new site typically follows a few distinct steps.

Firstly, the mining team identify a particular business challenge and the Petra team then determine if it is suitable for MAXTA.

Then, the site sponsors the next phase of work to be done, which often involves a business analyst or improvement specialist who captures the required data and supplies it to Petra to build into a model.

When the first model is complete, Petra submits it for stakeholder feedback.

This meeting normally becomes an engagement workshop where all parties check that the right areas have been modelled.

“This often turns into an iteration of the initial development process as stakeholders tend to redefine their priorities slightly,” Penny said.

“It allows for a sharper project focus and the model is adjusted accordingly.”

Users have several options when it comes to how they will engage with MAXTA.

Naturally, it is available as a stand-alone web application, but it can also be integrated with a number of industry leading software suites.

Outputs can be directed into OSIsoft PI, whom Petra has a technology partnership with.

Maptek Vulcan software also has a technology partnership with Petra and their mine planning software is also able to accept inputs from MAXTA.

In this way, users can access MAXTA from a platform they are already familiar with, in addition to the web app if necessary.

Feedback loop

The way in which MAXTA develops a model based on historical and current data, reconciles the model and then provides, predictive modelling, simulations and even suggests optimal designs – often with an accuracy in the range of 2-15pc depending upon the fidelity (Monthly <2 pc error, hourly +/-10-15pc accuracy) – sets it apart in the mining world.

It is difficult to see how a mining operation could achieve this level of detail and fidelity with conventional software and ‘good old-fashioned know-how’; methods that typically return error factors in excess of 20-40pc.

Mines often struggle to execute to plan due to the uncertainty in their geology, but with MAXTA’s ability to machine learn from data, mines can now get their best day, every day.

That is money in the bank.

More information:

Petra Data Science
07 3310 8774
info@petradatascience.com 
www.petradatascience.com.au

The post The best day, every day appeared first on The Australian Mining Review.

EQUIPMENT Placement’s patented CareTaker ALRS design has been developed specifically to address the ongoing issues associated with fluid sampling methods, and to remove safety hazards for operators and service personnel.

Equipment Placement co-founder and director Brian Bondi told the Australian Mining Review that there has been many variants of the project since its conception, but that it was product developer Jason Bondi who took the theory behind the objectives and transformed the product into the CareTaker ALRS.

And now, after more than one year of field testing, the CareTaker ALRS has hit commercial production and is hitting every target the Equipment Placement team set.

The concept was designed by Fluid Transfer Technology, which is a JV company specifically registered for the research and development of new innovative products to suit the ongoing demand in the mining industry for increased automation and higher standards of safety.

Fluid Transfer Technology has three equal partners, Brian Bondi, John Bondi and Luke McInnes, all of whom had some level of input into the concept and functionality of the product.

Product development was handed over to John’s son, Jason, who developed the ALRS into a commercially acceptable product.

How it works

The CareTaker ALRS has a sample control unit placed inside the operating station for safe initiation of a live oil sample, and a remote sample station placed in a convenient location that can be accessed when the machine is isolated.

Sampling is carried out using three simple steps.

Firstly, the unit is purged with fresh fluid, then a measured amount of fluid is collected in the sample chamber, before the sample chamber discharges the sample into the sample bottle.

The bottle is then sealed so that there can be no interference with the sample between testing and the lab, and this is one striking point of difference as it is just one more instance where human interference can no longer damage the integrity of oil samples.

Equipment Placement product developer Jason Bondi said that the unit could be mounted anywhere on the machine and plumbed into the fluid that needs sampling.

“To sample engine oil, we take a feed from a pressure line and a return line to the engine’s crankcase.  The controller is located inside the operator’s station, inside the machine and out of harm’s way,” he said.

“We like to think that, to the best of our knowledge, there is nothing comparable on the market.

“There may be products that offer a similar type scenario, but in our opinion they would not provide the same accuracy and the same result

“There are other products, but they still require manual handling to sample the oil.”

“There’s not another system where you can sit in the cabin and take samples.”

Not all samples are the same

Brian Bondi said that there were two major reasons for developing the product.

“Firstly, there is a lack of consistency when taking a manual oil sample which results in a lack of confidence in the actual sample being an accurate representation of the relevant compartment,” he said.

“Lack of consistency would then bring other factors into play such as the environment, atmospheric conditions or poor sampling techniques, meaning maintenance departments would get the samples but could never be confident of the results.

“You could take 500 samples per week on a site and it wouldn’t be uncommon for 200-300 of those actually being deemed compromised and therefore disregarded.”

This means sampling is a highly expensive and inefficient process.

Poor samples and bad sampling techniques were the first problem, but the second problem was equally important – taking live samples.

Live sampling is widely acknowledged as the best method for oil sampling, however due to safety risks, most mining companies steer well clear of the method to the detriment of sample quality.

Mr Bondi said that most non-live testing regimes ensure no workers can get within the footprint of an operating machine to carry out testing.

“Our system is remote or semi-remote depending on the needs of the client and it can be configured in any number of ways to suit clients’ needs,” he said.

“Safety is paramount on the mine site, and mining companies will look at safety first. Sampling will always come second.”

Why take live samples?

Live samples are simply better because they are more accurate, and they give a more accurate representation of the oil when it is in operation, meaning the analyst knows that the sample is representative of the component.

When a live sample is taken from a point in the engine, any contaminants detrimental to the component are suspended in the oil and therefore suspended in the sample.

Whereas when a sample is taken from a machine that has been turned off and allowed to settle, the oil has cooled down and some of the particles that would be detected in the live sample would no longer be suspended and therefor ‘non-representative’.

Mr Bondi said that the other reason is that oil samples must be taken from the same spot every time to ensure consistency.

“Common practice is to remove a cap from a reservoir or a dipstick from a dipstick tube and insert a plastic tube connected to a vacuum pump to extract a sample,” he said.

“The problem with this process is that it is near impossible to take the sample from the same point each time especially given that it will be done by multiple personnel therefore giving inconsistencies straight away.”

“Invariably this will lead to inaccurate samples, as the tube will take oil samples from the top, sometimes from further down, sometimes you run the tube along the edge of the component and pick up debris, and this is yet another issue with sampling techniques that needs to be addressed.”

What’s next?

The ALRS CareTaker has been going from strength to strength since its commercial launch in September.

The sales and marketing of the product will initially be through Equipment Placement Sales and Services, with the view to expanding distribution networks from 2020 onwards.

Interest has been shown offshore with EPSS receiving its first order from a South American Komatsu dealer.

While the company is vigilant for any teething problems that might arise, it is also looking to the future and to the full automation of the system.

Jason Bondi said that his focus was on developing the system for fully autonomous trucks which would not need any human interference at any stage.

“Right now, we’re working on different control methods,” he said.

“At the moment it’s a simple control method with a push button inside the cab, but we’re working on having a HMI feedback interface screen that will automate the process further and remotely operate the system.

“Those sorts of developments will become more prominent”.

“As more and more autonomous trucks enter the mining industry, the autonomous trucks will have an autonomous live sampling unit and the sampling will be automated to the point where the oil sample can be initiated from a remote location.

“We’re aiming for this to be ready mid-2020.”

More information:
Phone: (08) 9479 4988
Email: bbondi@equipmentplacement.com.au
Website: www.equipmentplacement.com.au

The post Automated live remote sampling appeared first on The Australian Mining Review.

BIG data is the new frontier of the modern mine site.

It is fast becoming the competitive advantage in the never-ending struggle against inefficiency on site, and the breakthroughs that big data can give us have been breathtaking.

Predictive maintenance through real-time monitoring is perhaps the single biggest area for efficiency improvement in the Australian mining industry, and haul roads are no exception.

During the average service life of a haul truck, it will need tyre replacements that will equal the value of the truck, due to the extreme load placed on tyres and the damaging conditions of roads.

Haul road maintenance has long been a tedious job that requires manual inspection and a lot of guesswork, so it was only a matter of time before engineering companies started looking to develop state-of-the-art tech for haul road quality control.

Proof Engineers has developed a system that takes the guesswork and expensive labour out of road maintenance.

The Road Condition Monitor (RCM) is an innovative, on-board road monitoring system that records and translates road condition data, without the need for an expensive, comprehensive communications backbone on the mine site.

The system has been specifically developed for tough mining conditions by Proof Engineers in direct response to industry need, and has the ability to reduce maintenance requirements, improve operational efficiency and autonomously monitor road conditions.

WHY USE IT:

• • • • • • • • Lower cost per tonne hauled
              • Improved production efficiencies
              • Optimisation of maintenance scheduling
              • Increased tyre life
              • Lower operating costs and fuel consumption

 How it works

Once installed onto the vehicle, data is wirelessly uploaded in real-time and displayed on the site’s secure platform.

Algorithms then calibrate and normalise data for true road conditions and prioritise maintenance scheduling.

The RCM is fitted with advanced sensors that map out the road quality, displayed in a simple colour code: from green signifying good quality sections of the road, yellow signifying satisfactory road conditions, orange where maintenance is required for medium-to-high rolling resistance, to red highlighting areas that need urgent maintenance or potential redesign.

By measuring the vibrations of deteriorating roads, the advanced algorithms of the RCM are able to convert vibrations into readings which then generate a user-friendly “road score”.

This score is used for site benchmarking, which in real-time, gives a quantifiable and objective assessment of road and pit conditions.

By using big data, the RCM is able to prioritise and target trouble areas on the maintenance schedule.

This achieves an improved running surface and drainage, and it reduces the overall rolling resistance on haul roads ultimately resulting in lower operational costs and a lower cost per tonne hauled.

How do you use it?

The RCM system is user-friendly and requires minimal training.

The 12/24v powered RCM units are installed onto haul trucks, water carts and light vehicles.

Numerous vehicles are fitted with the units and this ensures a more accurate and adequate live coverage of the roads at any given time.

The RCM units are self-calibrating and do not need complicated or detailed site setup.

Once installed, the units will start recording and uploading data instantly, and the online management platform can be accessed via the web by using a password-protected login system.

This system is designed with ease of use and flexibility as the main objective.

With minimal training, the system can seamlessly integrate into any existing operation and can be customised to meet site requirements needs and objectives.

The Proof difference

Proof Engineers is an Australian-based company with global projects and decades of experience in the civil and mining industries as owners, contractors and consultants.

The team’s broad mix of disciplines range from the practical, hands-on site personnel through to research and design engineers.

The company believes that road construction and methodologies are just as important as design when it comes to the performance of a haul road, and that there are five steps for achieving any road maintenance goal: site evaluation, design and stabilisation, site-tailored construction and training, maintenance and monitoring and reporting.

Proof Engineers excels in systems that help improve production for the mining industry.

The company improves operational efficiencies – specifically through haul road design, construction management, maintenance programs and monitoring solutions.

As well as being the leaders in haul road maintenance, Proof Engineers operates a Haul Road Development Program (HRDP) that designs haul roads by combining the best available materials for the job with construction practices to help mines drastically reduce their maintenance costs, while improving operating speeds and wet weather recovery.

Proof Engineers also specialises in dust monitoring systems.

Using a state-of-art mobile dust monitor called the Dustective, the company can measure dust emissions on roads and other sources.

The team at Proof Engineers can audit any mine site for environmental compliance and/or evaluate the performance of dust control measures.

More information:

Phone: (07) 5522 0855
Email: info@proofengineers.com.au
Website: www.proofengineers.com.au

The post Proof is in the data appeared first on The Australian Mining Review.

Problems or malfunctions in plant and equipment operation often become apparent through changes in vibration behaviour, unusual temperature patterns or noise emissions.

Reliability and condition monitoring specialists, Schaeffler, have almost 20-years’ experience in this field and primarily use vibration, temperature and acoustic emission diagnosis to detect damage or abnormal wear in machinery at a very early stage, so that these components can be repaired or replaced as part of planned downtime.  Costly unscheduled downtime is therefore minimised.

Depending on the type of machine and its importance in the production process, condition monitoring can be carried out by means of either continuous (online) monitoring or regular periodic (offline) monitoring.

Schaeffler condition monitoring and reliability systems manager, Mark Ciechanowicz, said the company has several systems available, ranging from single point to multi-channel surveillance systems utilising multi-parameter sensors.

“Predominantly we measure vibration, but we also monitor other condition monitoring parameters such as acoustic emissions, component temperature and oil and grease condition.  We can also accept other process parameters such as speed, load, pressure, etc,” he said.

“We have a Remote Diagnostic Centre (RDC) in Sydney where we measure 300,000 characteristic data points daily throughout Australia and New Zealand, which include assets like wind turbine generators, grinding mills, mobile mining equipment i.e. draglines, shovels and haulage trucks, fixed mining plant equipment, pumps in water sewage treatment plants. We also cover variable speed and ultra-slow applications.”

The Schaeffler diagnostic experts are familiar with all analytical techniques ? ranging from vibration measurements, infra-red thermography to torque analysis and endoscopy inspections – and as a result, they can quickly identify malfunctions and devise appropriate solutions.

Online monitoring options

When monitoring mobile fleets, such as haulage trucks, draglines and shovels, the focus is typically on electric wheel motors, alternators, hoist, drag, swing, propel and crowd sections, and is carried out with dual parameter sensors monitoring both the vibration and temperature of each bearing.

All the data is collected automatically and communicates over the Schaeffler cloud back to the RDC where Schaeffler’s reliability service engineers perform the diagnosis.

Should the 4G wireless signal drop out, the data is retained on the machine’s data logger and will automatically transfer to the Schaeffler cloud once reconnected.

“We provide reports based on machine condition back to the client – that happens on a regular basis, normally monthly, however alarms are checked on a daily basis and any exceptions are reported straight away to the customer,” Mr Ciechanowicz said.

This regularity provides more in-depth knowledge of the normal condition of the machine and deviations can be more easily detected – forewarning mine sites of any components on the brink of failure or parts that could need replacement in the near future.

“In between that comprehensive diagnostic report, we also look at any alarms that have been generated in between the monthly reports and immediately provide any machine conditions that are an exception back to the client, so they are instantly notified if there’s any kind of change in that machine’s condition,” Mr Ciechanowicz said.

“We also go further than that: not only do we detect problems, we can also provide a prognosis as to when the client needs to do something, and what exactly it is that they need to do and what kind of maintenance is required for a particular machine.”

Seamless integration

In addition to offering advice on selecting the right monitoring system, Schaeffler also offers turn-key solutions i.e. installing and implementing monitoring systems.

This not only includes hardware selection but also system configuration and, where necessary, its integration into existing data collection and reporting systems.

“We often install systems on critical equipment to monitor motor drives, gearboxes, trunnion and pinion bearings.” Mr Ciechanowicz said.

“Because the mill bearings are actually rotating at quite a low speed, we’re able to employ techniques such as high-gain accelerometer and acoustic emission sensors for monitoring these slow-speed bearing applications.”

Schaeffler can install systems from new or retrofit to existing infrastructure.

“With both new and retrofitted systems, we set up baseline thresholds and tweak those alarms specifically over time to ensure that we eliminate the risk of any transient alarming that may occur.” Mr Ciechanowicz said.

“We help sites to increase the reliability of their plants which in turn increases the uptime and availability of the machines to produce when they are required to process.”

Invaluable investment

Investment in such monitoring systems often pays for itself in the first year due to the reduced failure costs.

“We’re arming clients with the time to ensure they have the inventory – particularly those components with long lead times – to plan maintenance activities in a proactive manner,” Mr Ciechanowicz said.

He pointed to the example of bucket wheel bearings as one such component where forewarning is particularly advantageous.

“Those large bearings aren’t just sitting around.” he said.

“Especially if they’re a custom bearing and the client doesn’t have one sitting in stock, there’s going to be a lead time and the costs start to escalate because you’ve got transport and replacement costs and mounting downtime costs as well.”

The return on investment from a comprehensive condition and reliability monitoring system is phenomenal.

“We’ve done calculations on a dragline where we measure the hoist, swing, drag and propel sections and the cost wasn’t so much on the gearbox – even though it’s a half a million-dollar gearbox in its own right – it was actually in the cost of the downtime,” Mr Ciechanowicz said.

“If you look at the cost of the downtime, every hour that dragline is down (depending on the commodity prices) it can be quite significant and vary from around $40,000-60,000 per hour.

“If you don’t have a spare gearbox or bearings for the gearbox to replace, you could be looking at days of downtime, so you can see how the production costs quickly outweigh the cost of the replacement part itself.

“In one failure such as this, the client could have paid for the entire condition monitoring system across the whole dragline.

“So, our monitoring systems provide common-sense insurance for the client against this type of unscheduled downtime for all their mining plant and equipment.”

More information:

Schaeffler

02 8977 1010

Info.au@schaeffler.com

www.schaeffler.com.au

The post Forewarned and forearmed appeared first on The Australian Mining Review.