Certificate in Smart Maintenance for Enhanced Reliability
-- ViewingNowThe Certificate in Smart Maintenance for Enhanced Reliability course is a career-advancing opportunity for professionals in the industrial sector. This program focuses on teaching learners how to leverage data-driven predictive maintenance strategies, improving efficiency and reducing equipment downtime.
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GBP £ 140
GBP £ 202
Save 44% with our special offer
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โข Smart Maintenance Fundamentals: An introduction to the principles and practices of smart maintenance, including the benefits and challenges of implementing smart maintenance strategies.
โข Condition Monitoring Technologies: A study of various condition monitoring technologies used in smart maintenance, such as vibration analysis, infrared thermography, and oil analysis.
โข Predictive Maintenance: An exploration of predictive maintenance techniques, including data analysis, machine learning, and artificial intelligence.
โข Reliability-Centered Maintenance (RCM): An overview of RCM principles, processes, and best practices, including the identification of critical equipment and the development of maintenance plans.
โข Work Management: A review of work management practices and tools, including work orders, scheduling, and inventory management.
โข Internet of Things (IoT) and Sensor Technology: An examination of IoT and sensor technology, including the role of sensors in smart maintenance, data collection and management, and cybersecurity considerations.
โข Change Management: An exploration of change management principles and best practices, including the development and implementation of change management plans.
โข Continuous Improvement: A review of continuous improvement principles and practices, including the use of performance metrics, root cause analysis, and process optimization.
Note: The above list of units is not exhaustive and may vary based on the specific needs and goals of the certificate program.
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- Condition Monitoring: 45%
- Predictive Maintenance: 26%
- IoT & Sensors: 18%
- Data Analysis: 11%
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