Predictive Analytics

PREDICT AND PREVENT IMPENDING EQUIPMENT FAILURES WITH CONFIDENCE
Dingo’s NEW  Trakka Predictive Analytics solution utilizes artificial intelligence and machine learning to predict impending equipment failures with confidence, allowing you to proactively perform corrective maintenance actions to minimise downtime and optimise asset life.
 
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. 
 

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:

IMPLEMENTING THE SOLUTION AT YOUR OPERATION
Before any predictions can be made, Dingo’s domain experts and data science team work 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.

 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.

MAXIMISING 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:

  • Enterprise Resource Planning & Enterprise Asset Management (ERP/EAM) systems
  • Computerized Maintenance Management Systems (CMMS)
  • Fleet Management Systems (FMS)
  • All forms of condition monitoring data, including oil analysis, visual inspections, sensor data, vibration and thermography

For more information on Dingo's industry leading predictive analytics solutions, email us at info@dingo.com or use our contact form

Predict Impending Failures

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

Investigate hot spots

Review equipment condition to see which assets require immediate attention.

Plan what needs to be done

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

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.






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