Miners have recognized that there is a tremendous opportunity to increase operational efficiency, reduce costs, and improve safety with Big Data if their teams can access it, understand it, and apply it to real-world situations.
At Dingo, we are continuously evolving Trakka®, our award-winning, cloud-based predictive maintenance software to make it easy to capture and analyze an operation’s condition monitoring data and then extract its full value by transforming it into insights, actions, and outcomes.
Sensor data was proving to be particularly challenging for our customers to manage due to the volume and velocity of the data produced. In late 2017, Dingo launched Trakka 4.5 to help customers maximize on their sensor data. By enabling customers to capture and run powerful analytics on this high-frequency data, this version of Trakka is helping mines cut through the noise and turn a sea of sensor data into actionable intelligence that drives results.
Trend 2 - Instant Intelligence Everywhere
Mobile, cloud-based apps will deliver real-time access to insights that will drive better decisions—anytime, anywhere.
There is an app for almost everything in the consumer world. People don’t compartmentalize experiences, and the broad uptake of mobile apps to manage and act on information in peoples’ home lives is creating the expectation that this capability should be equally available in the work environment.
Based on customer feedback, Dingo identified maintenance and equipment health management as opportune areas for the development of a mobile app. To meet this need, Dingo developed a new Trakka Asset Health Management App™ that enables mine operators to access, understand, and act on their equipment’s condition anytime, anywhere.
This innovative new Trakka app, built by Dingo’s award-winning software development team, is part of a comprehensive suite of applications that provide a platform for equipment operators to see equipment health at a glance, recommend a course of action, and then track the progress through issue resolution, all from their mobile devices.
It provides a real-time view into the health of your entire fleet of equipment with the touch of a button. Using the power of Trakka’s predictive analytics engine, the app highlights areas of potential risk and immediately alerts users to impending issues. It also enables users to quickly and easily drill down into the condition of individual assets and components to get the full details. The user can then use the app to "Monitor", "Investigate", "Review", "Select", and "Escalate" the planned maintenance activities for any equipment under their responsibility.
Created with the future in mind, Dingo will be collaborating with its customers to develop a series of new functions to continuously meet their evolving needs, while empowering them to manage the health and maintenance of their equipment on the go.
Trend 3 - The Human – Machine Partnership
When applied to business processes with a clear purpose, AI, machine learning and technology are enhancing the human workforce.
The emergence of smart data, machine learning, and automation is empowering organisations to transform huge volumes of information into real-time intelligence and prescriptive analytics. These new capabilities are akin to crystal balls that operators can use to predict impending issues and recommend the optimal corrective actions based on prior outcomes.
To ensure that its customers are benefitting from the latest technological advances, Dingo partnered with the Queensland University of Technology to build a collection of Machine Learning models that can predict failure of equipment with a high degree of accuracy. These models will continue to evolve and improve over time, and the outputs of the project are already being used by some of our customers to help predict the Remaining Useful Life of major components.
According to Dingo, the key to developing successful solutions in this space is keeping people central to the process. Miners are gathering huge volumes of data and spending unprecedented amounts on technology, but are not always able to apply the analytics in practical, effective ways. It is critical to integrate the technology with real-life maintenance scenarios to help operators make smarter, more cost-effective decisions.