Predictive maintenance, mining maintenance, condition monitoring, mining IoT, machine learning in mining, AI in predictive maintenance, mining equipment reliability, vibration analysis, oil a ;

Course / Course Details

Application of Predictive Maintenance in Mining

Futuristic Mining & Technology

By - Super admin
3 students
20 Hours
(0)

Course Requirements

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.

Course Description

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.

Course Outcomes

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.

Course Curriculum

1 chapters
1 lectures
0 quizzes
20 Hours total length

Toggle all chapters

1 Introduction

Preview

1 Hour

Course Modules & Topics Module 1: Introduction to Predictive Maintenance in Mining Definition and importance of predictive maintenance Reactive vs. preventive vs. predictive maintenance Benefits of predictive maintenance in mining Module 2: Key Equipment for Predictive Maintenance Haul trucks, conveyors, crushers, and mills Underground mining machinery Pumps, fans, and processing plant equipment Case study: High-failure components in mining equipment Module 3: Condition Monitoring Techniques Vibration analysis for early fault detection Oil and lubricant analysis for wear and contamination detection Thermographic inspection for heat-related failures Acoustic emission monitoring for structural integrity Module 4: IoT and Real-Time Data Collection Sensors and IoT-enabled monitoring systems Wireless data collection and remote diagnostics Data integration with maintenance management systems Case study: Implementing IoT-based predictive maintenance in a mine Module 5: Machine Learning and AI in Predictive Maintenance How AI models predict equipment failures Machine learning techniques for predictive analytics Digital twins and simulation for maintenance forecasting Case study: AI-driven predictive maintenance in mining Module 6: Implementation of Predictive Maintenance Strategies Developing a predictive maintenance program Training personnel on predictive maintenance tools Overcoming challenges in PdM adoption ROI and cost-benefit analysis of predictive maintenance Module 7: Safety, Compliance, and Sustainability Reducing risks and improving workplace safety Regulatory compliance in mining maintenance Sustainability benefits of predictive maintenance Case study: How predictive maintenance reduces environmental impact Module 8: Practical Workshop & Capstone Project Hands-on predictive maintenance simulation Developing a predictive maintenance strategy for a mining site Final assessment and project submission Assessment & Certification Quizzes & Knowledge Checks (After each module) Practical Case Study Analysis Final Exam & Certification

Instructor

Super admin

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.

0 Rating