Defeating Downtime: Dingo takes the next step in predictive analytics

Release date: 1/9/2019

International Mining - Paul Moore homes in on the latest predictive analytics offerings to help keep mines running and potentially make unplanned downtime a thing of the past.

Machine Learning and AI are big buzzwords. A lot of companies are talking about them, but Dingo says it is leading the way in the practical application of this new technology. “Our new Anomaly Detection and Remaining Useful Life (RUL) models will be launched in Trakka®in early 2019 to bring immediate Asset Health benefits to our growing customer base. These models were built in collaboration with experts from industry and academia, using real customer data and targeted at specific industry problems.”

Dingo has spent the last 12 months developing and refining Machine Learning models, in collaboration with leading miners and the Queensland University of Technology, to detect anomalies in condition monitoring data in Dingo’s OEM independent global asset health database.

These models highlight anomalous behaviour in the data and will be available to users of Trakka®. As more quality component failure data is added to the data set, the accuracy of the anomaly detection models will improve. By detecting anomalies automatically, it will allow users to:

  • Detect developing issues well before traditional engineering limits are reached
  • Find slight changes in data trends, not discernable to a human analyst
  • Act faster to correct abnormalities and restore equipment to normal operating condition

Dingo is also developing sophisticated predictive analytics models aimed at predicting the remaining useful life of assets. Trakka users will have access to valuable analytical information about the Probability of Failure and Degradation Indexes. These models are built by Dingo subject matter experts for common asset specific failure modes, eg engine piston ring wear. They are designed with scalability in mind and can be easily retrained to work with a broad range of asset/failure mode problems experienced by real mining operations, making them highly reusable without further development. By creating an accurate Remaining Useful Life (RUL) model, it will allow users to:

  • More confidently plan component replacements
  • Optimise repair costs when components are nearing end of life
  • Improve related processes such as budgeting and supply chain logistics and management
For more information on Dingo's new predictive analytics offerings, email us at or use our contact form

Paul Moore │ International Mining │ Excerpted from the feature article – Maintenance: Defeating Downtime