Engineering & Mining Journal

MAY 2019

Engineering and Mining Journal - Whether the market is copper, gold, nickel, iron ore, lead/zinc, PGM, diamonds or other commodities, E&MJ takes the lead in projecting trends, following development and reporting on the most efficient operating pr

Issue link: https://emj.epubxp.com/i/1119397

Contents of this Issue

Navigation

Page 34 of 83

MAINTENANCE MAY 2019 • E&MJ 33 www.e-mj.com ments to plan for and prevent downtime and lost revenue. Prior to the official launch of Asset Health, Hexagon Product Manager and In- novation Lead Carl Brackpool offered inter- esting 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 ma- chinery. 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 pres- sures and electrical systems. "But there are so much more data coming off these machines: exhaust gas temperature, turbos, as well as all the en- vironmental 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 opera- tor brand new, just out of training? Did that machine just come out of the main- tenance 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 ma- chine learning, is going to say, 'I'm about to fail. But it's an opportune time be- cause 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 Con- struction Machinery, offers a three- pronged service-and-software approach as part of its ReadyLine maintenance pack- age. 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 mainte- nance, workflow and alarms processes to ISO 55000 readiness and more, accord- ing to the company. By using a combina- tion 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 mainte- nance 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 ap- ply data cleansing to create valuable con- text 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 convey- or 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 switch- gears and almost 400 drives, and hun- dreds of process controllers and servers. Asset Vista is a fundamental part of ABB's Ability Predictive Maintenance service. It pulls together previously dispa- rate 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, avail- able control system data, as well as in- formation from pre-installed expert condi- tion monitoring systems and datasheets. Technology advances are opening the door to market entry for new mainte- nance-related concepts, multiplying the options available to fleet and plant oper- ators for monitoring equipment status and usage to make better-informed mainte- nance plans. As an example, MachineMax, an off-road fleet management solutions provider that is majority-owned by Shell, recently announced it has integrated semi- conductor technology specialist Semtech's LoRa devices and wireless radio frequency technology into a new, smart off-road ma- chine usage-tracking solution. MachineMax said its devices can be de- ployed on to fleet machines in under a min- ute. They attach magnetically and don't re- quire an external power source or additional infrastructure to begin gathering real-time data on machine usage status. "With Sem- tech'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 man- agers, 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, reduc- ing fuel waste and maintenance costs." Semtech claims its LoRa technology solves many of the traditional radio-fre- quency design compromises involving range, interference immunity and energy consumption, and offers a low-cost solu- tion to connecting battery-operated devic- es 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 oper- ators are transitioning, either in full or in part, from preventive maintenance strat- egies in which planned upkeep is sched- uled 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 re- quired 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 trans- formation, 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 quan- tify. In one interview conducted during the report's information-gathering phase, a min- ing executive explained why. "It's a dynam- ic non-event...if the event had happened, it would have cost this much," they said. "Ac- countants have a hard time, because there's nothing that happens in the balance sheet." 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

Articles in this issue

Links on this page

Archives of this issue

view archives of Engineering & Mining Journal - MAY 2019