Predictive Maintenance of Mining Machines Applying Advanced Data Analysis

Martins, Paulo George Miranda; Soofastaei, Ali

doi:10.1201/9780429433368-7

Cited by 2 publications

(2 citation statements)

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“…Like any other complex industrial operation, the mining industry requires intense capital injection and, as such, requires maximum asset utilization and throughput [1]. However, the industry continues to face several challenges ranging from diminishing resources, deeper deposits, harder rock mass, high capital and operating costs, volatile markets, and increasing environmental and social awareness [2,3]. This can disrupt the operational success of mining companies.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, advanced data analytics and Machine Learning (ML) have been tipped to possess great value by extracting insights from operational data to enable process optimization, increased equipment efficiency, and timely decision-making [4]. With the amount of data generated from mining operations and advances in ML and data analytics, companies can make real-time decisions and predict future events to improve operations performance, reliability, and efficiency [2,5].…”

Section: Introductionmentioning

confidence: 99%

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2022

AMMS

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Advances in data analytics and Machine Learning (ML) techniques have seen a tremendous uplift in recent years and impacted most complex mining operations regarding safety improvement, operations optimization, and cost reduction. However, due to a lack of coding skills, most mining personnel face the challenge of fully adopting these techniques. In this project, a desktop application, Ute Analytics, was developed to allow users with little or no coding skills to perform data analytics and apply machine learning to any structured data. The app has an easy-to-use Graphical User Interface (GUI) and in-built data cleaning algorithms for users to import structured data, perform data cleaning, and store the cleaned data on their computers. Several Exploratory Data Analysis (EDA) tools were built into the app to enable users to explore trends and get insights from data. Additionally, the app has in-built machine learning tools-regression and classification algorithms-to give users the ability to train, test, and build machine learning models for predictive purposes. Finally, the app is made executable so it can be installed on any computer with Windows Operating System. With its user-friendly GUI, the desktop application was tested on real industrial data, and its ease of use proved that mining personnel and data enthusiasts with no coding experience could use it to benefit from data analytics without the need to understand or write complex computer codes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%