Predictive Analytics

Take Control of Your Operations
with the Power of Predictive Analytics

DINGO’s Trakka Predictive Analytics solution, winner of the 2019 Mining Magazine Software Award, utilizes artificial intelligence and machine learning to predict impending equipment failures with confidence.

It empowers you to proactively perform corrective maintenance actions to minimize downtime and optimize asset life.

DINGO Predictive Analytics Remaining Useful Life Models

DINGO has developed a series of sophisticated predictive analytics models that provide anomaly detection and failure prediction for asset-intensive industries.

These tried and tested models are built by uniting failure data from actual equipment, DINGO’s industry expertise and data science to address common component-specific failure modes, such as final drive gear teeth wear.

businessman

“With Trakka, I have the ability to see all of the important information in one place, which allows me to make better maintenance decisions. The system also integrates with our ERP system so that the corrective maintenance work identified flows directly into work orders. This type of end-to-end solution drives efficiency and helps us keep the focus on condition– not just tasks.”

Surface Maintenance Planner at a large gold mining operation in Nevada

The Power of Predicting the Future

Trakka’s Predictive Analytics solution, powered by our proprietary machine learning library, can predict the time until asset/component failure with a high degree of accuracy. Our customers will reap the benefits of these Remaining Useful Life (RUL) models as they:

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

Implementing the Solution at Your Operation

Before any predictions can be made, DINGO’s domain experts and data science teamwork with a customer’s historical failure and condition monitoring data to deploy or adapt existing models or create new machine learning models to correctly identify failures within the customer’s fleet.

This process typically involves data collecting, cleansing and validation to ensure model outputs are as accurate as possible. The transition to online predictive analytics is complete once the data ingestion pipeline is ready and the models are fully trained and tested.

Taking advantage of these insights doesn’t have to be left to the data scientists. DINGO’s code-free platform is easy to use and delivers the data you need to make informed decisions, giving you more time and confidence to get the job done right.

Close at hand, DINGO’s data science experts are involved in this entire process, to assist and ensure any customer feedback is incorporated into the models.

Reduce Downtime and Extend Equipment Life
Predictive Analytics Data Integration and Model Deployment

A Solution Designed to Scale

The models are designed with scalability in mind and can be easily re-trained to work with a broad range of asset and failure mode problems experienced by real mining operations, making them highly reusable.

The models are continuously optimized through ongoing validation and the input of new data and equipment performance information.

Connect with an expert to schedule a Trakka demo specific to your operation.

Maximize on All Your Data

DINGO connects a broad range of systems and software with the Trakka® Predictive Analytics platform to provide data surrounding your Asset’s health, including:
Condition Monitoring Data | Onboard Sensors | ERP Systems | CMMS | Mobile And Field Devices
PREDICT IMPENDING FAILURES
PREDICT IMPENDING FAILURES

Utilize artificial intelligence and machine learning to predict impending equipment failures with confidence.

Investigate Hot spots
INVESTIGATE HOT SPOTS

Review equipment condition to see which assets require immediate attention.

PLAN WHAT NEEDS
PLAN WHAT NEEDS TO BE DONE

Proactively manage any immediate risk and plan maintenance into the future.

Share and action as required
SHARE AND ACTION AS REQUIRED

Use the Asset Health Management App to quickly and easily share detailed reviews and recommended actions with the appropriate stakeholders.

Insights and Resources

The Practical Application of Predictive Analytics

The Practical Application of Predictive Analytics

DINGO’s Trakka Predictive Analytics solution utilizes artificial intelligence and machine learning to predict impending equipment failures with confidence, allowing miners to proactively perform corrective maintenance actions to minimize downtime and optimize asset...

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