INTERNET OF THINGS
The interlock status viewer provides an improved user interface to support efficient fault-finding and displays more information like time to trip to enable the transformation from reactive to proactive plant operation . The group step viewer shows all steps of a group sequence in a format easy to understand by operators . It raises awareness of potential changes in the process before starting a group sequence . The detailed status viewer for engineers and maintenance teams provides an innovative view that shows a global , functional representation of the process , all in the same chart . The detailed status report of its assets and the abnormal situations are all in the same chart . This helps provide information needed for quick analysis and action to resolve issues .
The Trakka condition management system was designed by mining engineers for maintenance operations , and utilises all of a mine ’ s condition data to improve decision-making . Plus , Dingo ’ s system is supported by a team of Condition Intelligence experts who have over 800 years of combined mining maintenance experience .
The maintenance manager of a large North American coal mine said the Trakka software developed by Dingo had major cost advantages for the operation . “ Trakka allows our team to run a highly effective condition-based maintenance program that helps our mine operate at the lowest cost per hour . One of the key points is that Dingo and Trakka help us manage risk by continuously assessing the condition of each component . We use this information to ensure we hit availability targets and deliver our mine plan .”
Moving up the data utilisation value curve
While the Internet of Things and Big Data are popular concepts and receiving a lot of industry attention , Dingo argues that most mining companies aren ’ t equipped to handle the advanced data capture , analysis and decision-making that are required to capitalise on this Big Data . “ We have seen very few mining organisations , including the biggest ones , with the systems and / or processes in place to manage and extract the value from the mountains of data being generated .” According to a McKinsey report , see www . mckinsey . com / industries / metals-andmining / our-insights / how-digital-innovationcan-improve-mining-productivity most miners use less than 1 % of the data collected from their equipment . “ Yet , in this same report , McKinsey highlights the incredible upside - an estimated $ 100 billion of economic impact in 2025 - of tapping into this latent data to optimise equipment maintenance in these areas alone : improve anticipation of failures ; reduce unscheduled breakdowns ; and extend equipment life .”
Based on decades of experience working with maintenance departments , Dingo ’ s point of view is that there is only value in data when it is applied with the end result in mind . All data should be put through the filter of " Will this information help improve maintenance outcomes and the health of the asset ? If it doesn ’ t check
Dingo ’ s Field Inspection App brings inspection data directly into Trakka for instant expert analysis
these boxes , it ' s simply creating noise in the system .”
The company adds : “ This is where technology applications such as predictive analytics and data management come into the picture . Used wisely , with the desired outcome in mind , these tools can process and analyse enormous volumes of data , leading to actionable insights that help miners make faster , better maintenance decisions and keep equipment performing at its best . While technology is an important part of the solution , the whole maintenance system , including people and processes , must be set up correctly to capitalise on these insights .” According to Dingo , that ' s a big opportunity area for many mining operations .
On a recent trade mission to Latin America , Dingo CEO , Paul Higgins , found that miners were generating volumes of data , but were struggling to produce practical insights that improved maintenance decisions . Higgins believes the issue stems from technology systems that can ' t handle the wide variety of data sources , along with technology providers ’ lack of maintenance expertise . “ If you ’ ve never worked in mining maintenance , it ' s difficult to know how to apply the technology in a practical way .”
Advanced vibration analysis
Predictive maintenance vibration analysis offers many benefits across multiple industries , including mining operations . Quick and early detection of problems and faults in complex machinery like conveyors is crucial to the smooth operation , reliability , and safety of mining operations and coal distribution . Safety in a coal-mining environment remains a paramount concern for miners and operators : the flammability of coal dust and the dangers of underground mining require stringent care of machines and plant environment to preserve the safety and lives of workers .
Azima DLI analysts conducted a survey and analysis of a longwall mine ’ s machinery as a demonstration of Azima DLI ’ s predictive maintenance techniques . After building a database and conducting a single day of data collection , the analysts discovered serious damage on crucial conveyors .
“ Left unchecked , this problem would have inflicted severe damage on the conveyor and shut down the plant for at least a week . The damage would also expose the plant and its workers to the risk of fire or explosion . Azima DLI ’ s fault detection averted a potential safety hazard and ensured continued production and distribution of coal . As a result , the mine instituted new measures to ensure smooth and efficient operation of the machinery as well as safety of personnel and property .”
International Mining | NOVEMBER 2016