Course / Course Details
By - Super admin
Participants should have:
A basic understanding of mining equipment and operations (recommended but not required).
An interest in maintenance strategies and industrial technology.
A willingness to engage in technical exercises and case studies.
Mining operations rely on heavy equipment and machinery that must operate efficiently to minimize downtime and maximize productivity. This course provides an in-depth understanding of predictive maintenance (PdM) techniques, helping mining professionals implement data-driven maintenance strategies to prevent equipment failures and reduce operational costs.
Participants will learn about condition monitoring, IoT-based predictive analytics, machine learning applications, and real-world case studies to improve maintenance planning and asset reliability in mining.
By the end of this course, participants will be able to:
Understand the fundamentals of predictive maintenance and its applications in mining.
Identify key mining equipment prone to failures and breakdowns.
Apply condition monitoring techniques such as vibration analysis, oil analysis, and thermography.
Use data analytics and IoT-based sensors to predict equipment failures.
Implement machine learning and AI in predictive maintenance strategies.
Reduce unplanned downtime and optimize maintenance scheduling.
Improve equipment lifespan and operational efficiency.
Develop a predictive maintenance framework for mining operations.
As the Super Admin of our platform, I bring over a decade of experience in managing and leading digital transformation initiatives. My journey began in the tech industry as a developer, and I have since evolved into a strategic leader with a focus on innovation and operational excellence. I am passionate about leveraging technology to solve complex problems and drive organizational growth. Outside of work, I enjoy mentoring aspiring tech professionals and staying updated with the latest industry trends.
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Intermediate
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