Predictive Asset Analytics Software for Mining Operation Improvements

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In any asset-intensive industry, organizations strive for optimal performance at all times. They want to limit downtime, control costs, and minimize safety and environmental risks while achieving maximum productivity. In the mining segment, the goals are not much different. With a number of challenges facing the industry, mining organizations must take every opportunity to improve.

One opportunity to drive positive change in mining operations has been created through the exponential growth of industrial data and analytics. Organizations can leverage this data to improve performance and efficiency with predictive analytics solutions. These solutions provide real-time information on the health and performance of critical assets, giving personnel the insight needed to make timely and informed operational and maintenance decisions.

Predictive Analytics for Mining Operations

Because mining operations require a diverse set of complex assets, many of which are mobile or in remote and hard-to-access locations, monitoring equipment health and performance can be a significant challenge. Compressors, generators, pumps, fans, blowers, heat exchangers, boilers, ovens, kilns, pulverizers, crushers, gearboxes and condensers are just some of the many assets that can be monitored using sensor data. This asset data can be used in maintenance programs to mitigate risks and ensure that critical equipment is operating as expected.

Using a predictive analytics solution can lead to the identification of issues that may not have been found otherwise. The software is able to identify anomalies well before the deviating variables reach operational alarm levels, creating more time for analysis and corrective action. Equipment performance can also be improved due the insights and early warning notifications provided by the solution.

Schneider Electric’s Avantis® PRiSM software is based on a proprietary algorithm that uses Advanced Pattern Recognition (APR) and machine learning technology. Existing machinery sensor data is input into the software’s advanced modeling process and compared to real-time operating data to determine and alert upon subtle deviations in equipment behavior. Once an issue has been identified, the software can assist in root cause analysis and provide fault diagnostics to help the user understand the cause of the problem.

Solutions can vary widely in complexity and difficulty, which has been a challenge in mining, however PRiSM is an intuitive system designed so that the user can easily configure it to monitor different types of equipment.

Predictive analytics solutions help mining personnel take advantage of the massive amounts of data available today and use it to make real-time decisions that have a significantly positive impact on throughput, recovery, equipment reliability and availability. Mining organizations can move from reactive to proactive maintenance by leveraging condition monitoring and predictive analytics solutions to spend less time looking for potential issues and more time taking actions to get the most out of every single asset.

To learn more, download our free industry solution paper.

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  • Hi Candice,
    I really appreciate you to publish such a great post. It is very good thing that now valuable software is available to keep eye watch on each mining operation activities and improvement.

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