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How Trakka Uses Predictive Analytics to Prevent Unplanned Downtime

Jun 14, 2021 | Insights

Preventing equipment failures and reducing unplanned maintenance is critical to productivity and the bottom line. Predictive maintenance with Trakka® will help better manage risk in your maintenance operation, eliminate unplanned equipment failures, and increase overall predictability, productivity, and safety.

What is Trakka?

Trakka is a powerful, cloud-based Predictive Maintenance software system designed to house all of your Asset Health data under a single roof. Trakka captures operating and condition data from your machines. Then, utilizing proprietary statistical and machine learning models, our advanced analytics solution detects impending issues that could lead to equipment failure and then prescribes corrective actions. This insight enables your team to proactively address these issues during planned maintenance to minimize downtime and optimize asset performance.

Trakka‘s predictive analytics models are developed by DINGO‘s team of data scientists using failure data from actual equipment and then validated by maintenance experts. DINGO‘s algorithms work with your existing condition monitoring data and boast greater than 85% accuracy.

Here are some additional benefits of using predictive analytics.

ReduceImprove
Unexpected Failures and downtimeComponent Life by acting earlier
Repair costs as scheduling is optimizedConfidence in planning component replacements
Loss of wasted potential in capitalEquipment availability and reliability
Unnecessary maintenance activitiesBudgeting and the bottom line
Personnel and process risk by creating a safe and more controlled environmentBusiness related processes such as procurement, logistics, and management

Deep Industry Expertise and Predictive Maintenance Technology

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

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. We make the data collection process simple by using pre-configured interfaces 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, and ERP work history.

Analyzing with Proven Technology and Human Expertise

When Trakka detects anomalous behavior that possibly indicates a fault, it diagnoses the problem and determines the corrective actions that will rectify the impending 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.

Creating Proprietary Actionable Insights

DINGO Condition Intelligence experts average 28 years of experience in the mining industry. And they have their secret weapon, DINGO benchmarks, the world’s largest fleet health database that makes it possible to deliver the best actionable insights for equipment maintenance.

Maximizing Asset Health

Trakka combines predictive analytics with workflow management, including closed-loop, continuous monitoring, and action tracking. Issues stay open and are closely tracked until your equipment returns to its normal operating state. This approach increases asset availability, improves asset health, and optimizes resource utilization.

DINGO Results

On average, DINGO customers see a 20% increase in components operating in normal condition, an increase in planned maintenance, improved availability, and a >10% gain in component life.

DINGO has generated over $750 million in cost savings for customers. Best of all, DINGO makes it easy to achieve these results. You get fast implementation with an average time to go live in four weeks, and DINGO customers typically achieve payback in six months or less with a 3:1 ROI.

Below is a case study for one of our clients.

A large Canadian copper mine partnered with DINGO to implement a comprehensive and holistic predictive maintenance program 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:

  • Organizing 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;
  • 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)-and the increased availability and extended equipment life resulted in savings of over C$5.5 million (US$4.2 million) per year.

Implement Predictive Maintenance at Your Operation

Unlike traditional maintenance methods, DINGO‘s Trakka provides early warning indicators and insight that results in increased availability, fewer unplanned failures, and a reduction in overall maintenance time and cost.

Contact DINGO for a demo of Trakka or use our Savings Calculator to estimate the annual savings that your operation could achieve.

businessman

“Trakka allows our team to run a highly effective condition-based maintenances program that helps our mine operate at the lowest cost per hour.”

– Maintenance Manager at a leading North American coal mine

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