Maintaining the Pace
Fleet and plant operators can pick from a wide range of maintenance software, systems and platforms, but without a solid asset management strategy in place, even the best choice probably won’t provide optimal results

By Russell A. Carter, Contributing Editor

Not all types of mine equipment are created equal: some are more important to production than others,
and not all require the same level or kind of maintenance. A mine-wide equipment strategy is needed to
ensure that each item gets the maintenance attention it warrants. (Photo: Liebherr).
Maintenance is a key element in achieving operational goals for almost every segment of mining activity. At the most fundamental stage of mine or plant operations, equipment performance and health have a direct role in assuring worker safety and productivity. And, at the highest echelons of mine management, maintenance planning demands a level of attention commensurate for an activity that accounts, on average, for about thirty cents or more of every operating- cost dollar. Too much or not enough “wrench time” — in other words, over- or under-maintenance — can sink carefully crafted production plans and punch holes in otherwise-solid operating budgets.

It’s also the mine function with the widest selection of tools and options for getting the job done and measuring the effectiveness of planning, scheduling, task performance and outcome. In the software and systems area, these can range from simple work-order generation and tracking apps all the way up to enterprise asset management platforms that encompass everything from maintenance-related human resource allocation to spare-parts inventory control. Here’s a quick look at a selection of new-generation solutions designed to make maintenance more cost-effective and effi- cient, along with advice from maintenance experts on structuring critical maintenance processes and policies.

What’s Out There
There are literally dozens of maintenance software products available from a variety of sources, including major fleet-management solution providers such as Modular Mining, Hexagon, and Wenco, which offer maintenance programs as standalone applications or as modules in their comprehensive FMS suites. Others are geared more toward fixed-asset condition monitoring, such as ABB’s Ability Asset Vista. The latest generation of maintenance programs take advantage of the data granularity, management capabilities and flexibility provided by the Internet of Things (IoT), cloud computing, mobile devices and artificial intelligence (AI), and in many cases are designed to fit comfortably within larger enterprise asset management platforms.

Modular, for example, has offered its MineCare maintenance product since 2003. It is now in its third iteration, as MineCare 3 — a cloud-based, Software as a Service (SaaS) program that functions as a separate application or as a module within Modular’s Dispatch fleet management system. According to the company, the program’s SaaS subscription structure eliminates expensive IT infrastructure investments, improves data storage efficiency, and simplifies installation, upgrades, and management. SaaS, said Modular, doesn’t require capex investment and has modest network communication requirements. Its capability to scale according to user needs allows mines to monitor and manage a single machine, or thousands of units across an entire enterprise, from one centralized location.

Earlier this year, Hexagon’s Mining division released HxGN MineOperate Asset Health, described as “a platform of servers and data-loggers that will extend the life of mining equipment. Asset Health will help maintenance and operations staff to identify machine health trends in real-time, empowering them to improve efficiencies and minimize equipment downtime.”

The company said integrating Asset Health with its fleet management system (FMS) on a common hardware platform that efficiently uses networking and server resources simplifies installation for existing FMS customers. Asset Health can be used to connect OEM-agnostic platforms for onboard data logging, telemetry and messaging. Onboard alerts can be synchronized with the office, dispatch, reliability engineering and maintenance centers.

According to Hexagon, future development will offer Asset Health maintenance analytics with machine learning models to improve predictive maintenance capabilities, enabling maintenance departments to plan for and prevent downtime and lost revenue. Prior to the official launch of Asset Health, Hexagon Product Manager and Innovation Lead Carl Brackpool offered interesting insight into the development team’s design approach during an interview on the company’s Spotlight podcast series.

Brackpool said: “If you think about all the sensors on board a large piece of machinery. Let’s take a haul truck, that’s the most common piece of machinery that’s constantly working and it’s traversing the greatest distance in an open-pit mine, from the bottom of the mine over maybe even an hour to get all the way out to a dump, or a stockpile, or a processing plant. There are as many as 6,000 sensors or data points on board, and the normal ones you look at are tire pressure, brakes, hydraulic pressures and electrical systems.

“But there are so much more data coming off these machines: exhaust gas temperature, turbos, as well as all the environmental data, things that are offboard that machine. You know, what time of day is it? What’s the barometric pressure? Is it raining? We look at the CRM data and the human resources data. Is the operator brand new, just out of training? Did that machine just come out of the maintenance bay? If you start stacking all that data together, you’re going to increase the amount of noise, but our data scientists are writing amazing algorithms that scrub away all that noise, and they get it back down to a very pure data set, and they start looking for anomalies, things that don’t belong in there.

“And if you look at that pattern over a period of time, that [truck], through machine learning, is going to say, ‘I’m about to fail. But it’s an opportune time because I just happen to be between shifts,’ or, ‘I’m going to go to the main yard.’ So anyway, in short, that’s really what we’re doing here with the data collection in real time and processing of that data.”

Wenco, a subsidiary of Hitachi Construction Machinery, offers a threepronged service-and-software approach as part of its ReadyLine maintenance package. Through its ProActive maintenance service, it can provide consulting experts to assist in analysis of equipment data, maintenance records, and other details to optimize maintenance processes and practices before implementation of the ReadyLine program. These services range from planning optimization, lean maintenance, workflow and alarms processes to ISO 55000 readiness and more, according to the company. By using a combination of ReadyLine OEM sensor-monitoring software, business intelligence tools, and third-party systems, the company said its experts can create predictive maintenance capabilities that lead to better maintenance planning. Areas of focus include asset health condition monitoring, safety conditions monitoring, and failure mode and effects analysis (FMEA). Finally, the ability to connect with Hitachi’s Lumada or other enterprise-wide machine learning and AI platforms allows ReadyLine to apply data cleansing to create valuable context required to use mine data effectively.

E&MJ reported last year on how ABB’s Asset Vista is helping Vale manage the maintenance of stackers and the conveyor system at its S11D iron ore project in northern Brazil. According to the company, Asset Vista allows the mine to monitor the functioning of 6,000 critical assets on the site, including hundreds of transformers and large motors, more than 1,500 switchgears and almost 400 drives, and hundreds of process controllers and servers.

Asset Vista is a fundamental part of ABB’s Ability Predictive Maintenance service. It pulls together previously disparate condition data from various assets to collectively analyze and compare all data, enabling ABB to provide forewarning of a potential fault with a proposed solution, in time to address it before production is affected. Critical analysis of the assets takes into account failure modes, available control system data, as well as information from pre-installed expert condition monitoring systems and datasheets.

Technology advances are opening the door to market entry for new maintenance- related concepts, multiplying the options available to fleet and plant operators for monitoring equipment status and usage to make better-informed maintenance plans. As an example, MachineMax, an off-road fleet management solutions provider that is majority-owned by Shell, recently announced it has integrated semiconductor technology specialist Semtech’s LoRa devices and wireless radio frequency technology into a new, smart off-road machine usage-tracking solution.

MachineMax said its devices can be deployed on to fleet machines in under a minute. They attach magnetically and don’t require an external power source or additional infrastructure to begin gathering real-time data on machine usage status. “With Semtech’s LoRa Technology, we were able to create simple, easy-to-deploy solutions, which effectively monitor machine status from anywhere on a mining site,” said Amit Rai, CEO at MachineMax. “Real-time data from the sensors is presented to site managers, offering tangible insight into their fleet’s efficiency. Managers can use this data to identify problem areas at their site, and work to reduce machine idling, reducing fuel waste and maintenance costs.”

Semtech claims its LoRa technology solves many of the traditional radio-frequency design compromises involving range, interference immunity and energy consumption, and offers a low-cost solution to connecting battery-operated devices to the network infrastructure.

From Preventive to Predictive
As machine-health data collection and analysis technologies steadily improve, an increasing number of fleet and plant operators are transitioning, either in full or in part, from preventive maintenance strategies in which planned upkeep is scheduled according to usage or time-based triggers, to predictive maintenance that compares measured physical parameters with known operating limits. This allows equipment problems to be detected and corrected before a major failure occurs.

The benefits to be derived from the improved data integration capabilities required by predictive maintenance can, in some cases, be a tough sell to corporate, however. According to a report just released by Rockwell Automation on the progress to date of mining’s move toward digital transformation, financial departments may have difficulty recognizing digital value that is not readily apparent on a balance sheet. For example, predictive maintenance that helps avoid a repair cost can be difficult to quantify. In one interview conducted during the report’s information-gathering phase, a mining executive explained why. “It’s a dynamic non-event...if the event had happened, it would have cost this much,” they said. “Accountants have a hard time, because there’s nothing that happens in the balance sheet.”

RACI, explained.
To make predictive maintenance truly effective, most maintenance experts say it’s crucial to have an equipment strategy in place to prioritize objectives, ensuring focus is put on the highest-impact items first. Partners in Progress (PiP), a global management consulting firm, pointed out that because many large companies have too many pieces of equipment to be able to address them all in the same way, a defined strategy creates stability and momentum among staff as they are able to focus on a manageable number of objectives rather than a seemingly infinite number for all equipment. Once equipment has been prioritized, it becomes easier to systematically maintain critical items. This approach prevents management from trying to do everything at once and incurring the risk of doing it poorly. Organizations without equipment strategies run the risk of having to manage both breakdown costs and rising over-maintenance costs.

Bluefield AMS, another maintenance consultancy, listed in a recent blog the primary reasons for having structured asset management. According to the company, a well-constructed asset management plan actually extends beyond maintenance, and should not be a “set and forget” document that sits on the shelf in a maintenance manager’s office: It is the core document that enables cross-functional alignment and agreement on how a machine or area of the plant will be maintained and how it will be shut down in a scheduled manner. It should contain several key elements, including:
• Asset productive life – It is important from the outset to lay out the targeted productive life and the drivers that contribute to it. This should have input from mine planning, production and maintenance as the anticipated life can influence the maintenance strategy to ensure the machines meet the needs without over or under cutting maintenance. It is essential that this aspect is reviewed as part of the life of asset and five-year planning process.
• Operational context and operational limits – It is essential to understand these aspects in order to build an appropriate asset plan. Additionally, operational limits of the machine should be documented here to allow the condition monitoring strategy to monitor any adverse operating conditions and report to operations in a manner that enables them to take concrete action.
• Scheduled downtime strategy – Details the times the machine will be stopped for scheduled maintenance. It should also include opportune maintenance such as operator pre-starts. A scheduled downtime availability can then be calculated to show the potential of the machine.
• Condition monitoring strategy – It is important to clearly articulate all the condition monitoring tasks that are expected to be completed on the machine and at what frequency. It is important document what is achievable and practical for the application.
• Component change-out strategy – This should document every component to be changed on a scheduled or expected frequency. Consider this list as a major source of items to be loaded into the maintenance planning system.
• Life cycle cost model – A zero-base cost model should be built containing all the items covered in the strategy, with allowances for general and unscheduled repair.
• Stakeholder signoff – This is the most important part of the document because it is considered a working agreement signed off by all parties involved. These should include the mine planning manager, production manager, maintenance manager, and ideally, the OEM representative. The signoff should signify an agreement on how the machine will be operated and maintained throughout its life.

According to Bluefield, the asset management plan should be a live document that is reviewed at least annually and should align with budget generation. This will ensure any changes in strategy from operations to maintenance can be captured and reflected in site operation.

PiP said another critical step in the path toward predictive maintenance success is to specify well-communicated, simple roles and responsibilities for predictive maintenance personnel and others in the organization that will be involved in the process. For example, a simple RACI chart can be used for communicating the process and clarifying specific roles for all functions associated with the process. RACI is a synonym for who is “Responsible, Accountable, needs to be Consulted, needs to be Informed.” This ensures that all actions are implemented — a prerequisite for effective predictive maintenance.

And yet another important step is to set regular and formal reviews of performance using Results-Action-Reviews (RAR) at all levels of the maintenance department. A RAR consists of the following steps:
• Review results – Did it work? How are the KPIs tracking?
• Review actions – Did we do what we said we’d do?
• Agree on future results and actions.
• Prioritize future actions.
• Assign resources to advance highest priorities.
• Communicate key information.

RAR implementation creates a closed loop to review performance by tracking actual KPI results against targets, using pareto charts to highlight problems and take appropriate actions. This is then followed with supervisors doing effective short interval control to ensure, for example, that those actions are performed on schedule.

Martin Provencher, industry principal for mining, metals and materials at OSIsoft, noted in a recent white paper that the ultimate level of predictive maintenance is now “PdM 4.0,” a stage that moves from dependence on planned events to being able to take real-time action from actual events. According to Provencher, this represents “prescriptive maintenance” that can cut the time required for planning by 20% to 50%, increase equipment uptime by 10% to 20%, and reduce overall maintenance costs by 5% to 10%.

Main elements of OSIsoft’s PI System, an open-enterprise data-collection platform.
Implementing it usually requires a multi-step process that begins with establishing an operational data infrastructure — such as OSIsoft’s flagship openenterprise data collection platform, called PI System — to capture data, followed by enhancing and conceptualizing the data. In other words, giving it meaningful context. The third step, implementing condition- based monitoring, serves to identify the conditions that lead to an eventual failure of components on an important asset, and prepares an organization for full PdM 4.0 — the ability to apply analytics and pattern recognition tools to provide real-time, actionable intelligence to automatically determine patterns that lead to an eventual machine failure.

PI System has been used by several producers, including Syncrude, a major player in the Alberta oil sands industry. In an example provided by OSIsoft, Syncrude wanted to apply event synthesis on their mining equipment fleet for early intervention of maintenance problems and to reduce costs. The fleet also includes a large number haul trucks as well as other production and support equipment such as shovels, graders and dozers. Manual analysis of truck sensor data sets proved too cumbersome for timely analysis and intervention.

By using OSIsoft’s PI System to create a solution for reporting mechanical events occurring on the equipment, they were able to optimize and streamline calculations and integrate these with notification systems as well as validate and tune performance. This involved collecting data from 6,600 data points on 131 heavy-duty trucks and five shovels. The results, according to OSIsoft, were impressive. Syncrude calculated that fleet operating expense savings came to $16.75/hour per unit, which equates to a $20 million annual operating cost avoidance, not including probable lost production hours.

AI Gains Ground
Over the past year or so, mention of AI has crept into almost every crevice of the mining technology landscape, and maintenance is no exception. Producers are turning to AI for deeper insight into big data in order to recognize trends and determine decision points.

In January, Vale SA inaugurated an Artificial Intelligence Center at Tubarão in Vitória, Brazil, that will serve all of Vale’s operations around the world. The company said teams connected to the AI Center were working on 13 projects jointly with the company’s ferrous, base metals and coal business areas. The primary focus was on optimizing maintenance of assets such as its haulage trucks and railroad facilities, along with improving management of ore processing and pelletizing plant processes, improving environmental controls, health and safety prevention and corporate integrity enhancements.

Teck Resources has used sensors and data to monitor the health of haul trucks and manage repairs and preventive maintenance since 2011. It’s now using machine learning — a branch of AI — to take another step forward, through a partnership with Google Cloud and Pythian, an IT products and services company. Teck said it is “… unlocking new insights from the millions of data points generated by our mobile fleets. Issues that were previously unpredictable, such as potential electric failures, are now being identified before they happen by machine learning algorithms. We are also modelling and predicting remaining life span of our trucks, determining wear and wear, identifying abnormal failures and enhancing alarm and notification systems.”

Meanwhile, AI technology specialist Uptake and Chilean copper producer Codelco are working together to support Codelco’s digital transformation. Uptake said Codelco will deploy AI to monitor the health of mining equipment to anticipate maintenance needs.

The current agreement involves mining and processing equipment at Codelco’s Division Ministro Hales (DMH) mine in Calama, Chile, including haul trucks, grinding mills, roasters, crushers, pumps, among other equipment with a view to creating an enterprise-wide Asset Performance Management solution across all Codelco operating mines.

Uptake, which a few years ago helped Caterpillar develop a digital analytics platform, said its APM software solution improves operational efficiency by leveraging AI to create value from operational data. Its flagship product, Uptake APM, builds on what was formerly known as Asset Perform, a product used widely across major industrial sectors.

The company said Uptake APM integrates key features of what was formerly Asset Performance Technologies’ Preventance maintenance solution, including the Asset Strategy Library (ASL) — touted as the world’s most comprehensive database of industrial content including equipment types, failure mechanisms and maintenance tasks. Uptake acquired the ASL through its 2018 acquisition of Asset Performance Technologies.

What’s Ahead?
It’s clear that emerging technologies and strategies such as those listed above as well as Augmented and Virtual Reality, 3D printing of components, and vendor-managed parts inventory, to name just a few of many promising concepts waiting in the wings, offer enormous potential to boost maintenance productivity in the coming years. Most of those benefits will ride on the back of increased digitization initiatives that, in turn, require reliance on sophisticated sensors, faster data communications and higher computing power. The question is, will the ultimate outcome validate the oft-repeated promise of high-tech evolution: to uncomplicate workers’ jobs in an increasingly complex industrial environment?

As featured in Womp 2019 Vol 05 -