DINGO’s Trakka Predictive Analytics integrates offline with real-time data to improve outcomes.
Another platform that leverages artificial intelligence and machine learning to predict impending equipment failures, Trakka Predictive Analytics allows customers to “proactively perform corrective maintenance actions to minimise downtime and optimise asset life”, according to the company.
The solution is powered by a proprietary machine-learning library, which enables it to predict the time until asset/component failure with a high degree of accuracy, DINGO claims.
Gary Fouché, Chief Information Officer of DINGO, said the company has a range of customers using AI to help identify and address issues on specific fleets of mobile equipment and in the fixed plant environment, but there are specific areas where these have gained traction.
“Haul trucks have been a major pain point for our customers, and we have developed failure models for final drives, rod bearings, cylinder wear and fuel pumps,” he told IM.
“Another example is DINGO’s Anomaly Detection models being used to detect anomalies on data from on-board systems in a haul truck for a large gold miner,” he added.
With the wider DINGO Asset Health Process platform, DINGO is able to integrate all of these AI elements into the wider maintenance workflow, embedding work recommendations directly into the enterprise resource planning (ERP) and computerized maintenance management system, according to Fouché.
“This focus on integration into existing processes and systems helps ensure our customers can use this new information, while creating a seamless experience,” he said.
And, DINGO is already looking to advance its AI capabilities to the point where it could deploy machine-learning models to the edge or in the field.
Fouché said: “DINGO is working on several use cases where AI models are deployed onto devices that can be disconnected from the network and these models can undertake prediction and classification without a round-trip to the cloud.”
Daniel Gleeson │International Mining │ January 2020 – Excerpted from Mine Maintenance feature Trust or Bust