Proving next-generation value innovation is its overarching mission, global predictive maintenance leader Dingo has been awarded the honor of best software development from the industry and editors of Mining Magazine for Trakka® Predictive Analytics.
Development entailed two years’ work and resulted in a product that marries machine learning with maintenance expertise to predict impending equipment failures with a high degree of accuracy.
This foresight allows customers to perform corrective maintenance at the optimal time, significantly reducing downtime and optimizing the lifespan of the asset.
According to Dingo Director of Product Engineering Colin Donnelly, “Trakka’s capabilities equip miners with the knowledge to make maintenance decisions based on a component’s current health and what companies themselves determine as most cost and time-efficient.”
The Trakka Predictive Analytics solution consists of two distinct but equally important software models: Anomaly Detection and Remaining Useful Life (RUL).
The Anomaly Detection system was the culmination of 12 months of joint work by Dingo and Queensland University of Technology. This system detects abnormalities well before equipment’s traditional engineering limits are reached, enabling maintenance teams to address issues earlier and restore equipment to normal condition.
The RUL model predicts how long assets are likely to remain in operation and provides detailed analytical information through its Probability of Failure and Degradation indices. The result is enhanced ability to plan component replacements, optimize repair costs, and improve processes, such as budgeting and supply chain management.
Donnelly said Dingo’s competitive edge lay in its use of actual failure data to resolve equipment issues, in addition to its direct integration with ERP and computerized maintenance management systems.
“We can translate results generated by models into the next steps companies need to take to maximize the health and life of their equipment with minimal cost and interruption,” Donnelly said.
Given Dingo’s resolute focus on using real-world data, it is not surprising that Trakka has returned concrete results for its customers, including several Top 10 global miners.
According to Chief Information Officer Gary Fouché, while many systems built by other companies make lofty claims about their predictive analytics capabilities, they are missing the most vital element: practical application.
“There is a big difference between passing data through a generic analytics platform and Dingo’s solution. We work with customer failure data to develop models designed to help them gain better insight into the underlying issues so they can address the root cause.”
“Dingo is currently working with its customers to prioritise and build more AI models to address a broader range of problems,” Fouché said. “We are also extending Trakka’s machine learning capabilities to other areas, including the deployment of AI models to the edge or in the field.”
By tapping into the power of predictive analytics, miners will be able to anticipate the future and proactively manage equipment maintenance, while reaping huge productivity, planning and safety benefits in the process.