Fleet Management - Clean Bill of Health

Release date: 9/30/2018

Mining Magazine - Equipment health monitoring can be used at mine sites to optimise production and maintenance schedules, allowing mines to move material in the most efficient manner with the least amount of downtime. 

Dingo’s predictive maintenance platform seamlessly connects to all an operation’s condition monitoring data sources, including onboard sensors, fluid analysis, visual inspections, vibration and thermography. VP of Product Development and mining engineer, Colin Donnelly Mining-Magazine_Fleet-Management_Dingo-The-Global-Leader-in-Predictive-Maintenance_website-(1).pngsays: “By housing all the data in one system, Dingo can run powerful predictive analytics and prescribe the precise maintenance actions that will minimise downtime and keep equipment running in peak condition.”

The parameters are quite varied: they can be wear metals, contaminants and oil properties from oil analysis; temperatures, pressures and shift times from onboard sensors; and images and text from visual inspections.

“Some of the condition monitoring data is batch type data,  only collected during an equipment preventative maintenance task every 250-500 hours,” comments Donnelly. “Other information from onboard sensors is transmitted at a higher frequency, from a daily average down to being collected and transmitted every few minutes. Dingo’s system was designed to ingest and analyse both batch and real-time data.”

In the realm of sensor data, Dingo doesn’t believe that there’s significant value in replicating the alerts and alarms that are already available in the operator’s cab. Donnelly says: “The real value stems from using the onboard data in predictive analytics and machine learning models to identify emerging problems as early as possible, so they can be addressed during a planned maintenance task.”

A large Canadian copper mine partnered with Dingo to implement a comprehensive and holistic predictive maintenance programme and hit best-in class availability and life targets. The mine had been condition monitoring with oil analysis, vibration and thermography for years, but its fleet health and life were not achieving desired targets. The plan was to purchase two new trucks to supply the production capacity needed.

To address these challenges, the mine partnered with Dingo, which provided critical support in the following areas:

  • Organising information so the focus is on component health management and not just monitoring condition;
  • Providing recommended corrective actions to local team and tracks issues through resolution;
  • Using predictive analytics to identify emerging issues and plan condition-based work for existing maintenance windows;
  • Serving as a continuous source of expertise; and
  • Providing structure and focus through dashboard key performance indicators (KPIs), process control and benchmarking.

As a result, the mine didn’t need to buy the two new trucks – saving C$9 million (US$6.8 million).  The mill is operating with less than 1% unscheduled downtime in recent months, and the increased availability and extended equipment life are saving the mine over C$5.5 million (US$4.2 million) per year.

At another mine, the company had a A$150,000 (US$109,000) dozer engine that was on the path to catastrophic failure after only 4,000 hours of operation; however, the lab ratings pointed to a steady state of operation and weren’t catching the underlying issues.

Dingo’s Trakka predictive analytics engine identified and correlated several contaminants that were at critically high levels  and deduced combustion issues in the engine.  As a result, Dingo’s condition intelligence experts immediately issued a work order with the steps to pinpoint the root cause of the combustion issue – these checks identified that four exhaust valves and an injector were not within OEM specifications.  The issue was rectified, combustion returned to normal, and the dozer was cleared for operation.

Trakka identified an otherwise undetectable combustion issue before damage occurred, while its workflow management system enabled the maintenance team to quickly find and fix the root cause. As a result, the engine rebuild cost was avoided by the mine.

Excerpted from an article by Ailbhe Goodbody │ Mining Magazine