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How Using Predictive Maintenance Can Extend Machine Life

Jul 6, 2021 | Insights

Preventing equipment failures and reducing unplanned maintenance is critical to productivity and the bottom line. Mining is equipment intensive; the equipment is expensive to maintain, operate and replace.  This cost of unplanned downtime is a major reason why waiting for assets to fail is not an option in the mining industry.

So how do you reduce the chance of unplanned equipment failure, reduce operating cost, extend component life? Some mining operations are turning to IoT or IoE to find out how using predictive maintenance can extend machine life.

What are IoT and IoE?

The Internet of Things (IoT) allows for communication between people, processes and things. Wikipedia defines it as, “The Internet of things (IoT) describes the network of physical objects, so known as, “things” — that are embedded with sensors, software, and other technologies that is used for the purpose of connecting and exchanging data with other devices and systems over the Internet.

The Internet of Everything is a concept first created by Cisco. Cisco defines the Internet of Everything (IoE) as “the networked connection of people, processes, data, and things. The benefit of IoE is derived from the compound impact of connecting people, processes, data, and things, and the value this increased connectedness creates as “everything” comes online.”

IoT and IoE are often interchanged, but Cisco describes them differently. For example, IoT is described as a single technology transition, while IoE comprises many technology transitions (including IoT).

Predictive Maintenance and IoT?

Predictive maintenance is an effective maintenance strategy that forecasts machine failure and helps identify underlying problems that need to be fixed. This strategy combines sensor data and information about the equipment to predict failure.

How does this work in practice?

DINGO uses a unique combination of people, technology, and data to provide actionable insights that improve asset health, increase productivity, and extend machine life.

Collecting Data

With Trakka’s seamless integrations and in-field data collection capabilities, all real-time and batch condition monitoring data can be ingested into one platform and analyzed simultaneously. In addition, pre-configured interfaces are used to connect with your existing data sources and IT infrastructure.

Listening to Assets

Trakka uses a state-of-the-art predictive analytics platform to analyze and correlate all the incoming data, including:

  • Onboard sensors
  • Machine operating data
  • Fluid analysis
  • Visual inspections
  • Vibration
  • Thermography
  • ERP work history

Proven Technology and Human Expertise

Trakka detects anomalous behavior that possibly indicates a fault; it diagnoses the problem and determines the corrective actions to rectify the upcoming issue. Our Condition Intelligence™ experts then blend the analytics with their expertise to prescribe the maintenance actions to keep your fleet operating at peak performance.

The Impact of IOE for Mining Companies

Currently, many mining operations use a reactive maintenance strategy where assets are pushed to failure. Unfortunately, a reactive maintenance strategy reduces asset health and life and can be incredibly damaging to the bottom line for any operation.

Predictive Maintenance and IoT with DINGO can improve availability and component life by up to 15%. And a recent study by Accenture and GE found that predictive maintenance can generate a 30% reduction in maintenance costs and as much as 70% cut in production downtime.

For more information, contact a DINGO expert.


“Partnering with Dingo allows us to leverage remotely located condition intelligence experts from the get-go for a fraction of the normal cost to company. This allows our few reliability experts to focus on the top issues and make important decisions to ensure our fleet availability is where it needs to be instead of having them analyzing data and looking for the issues.

-Martin Pichette, Mine Operations and Maintenance Director at Lamaque Mine

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