Global Certificate in The Science of Predictive Maintenance
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⢠Introduction to Predictive Maintenance: Defining Predictive Maintenance (PdM), its benefits, and how it differs from reactive and preventive maintenance. Understanding the role of condition monitoring and data analysis in PdM.
⢠Sensors and Data Acquisition: Types of sensors used in PdM (vibration, temperature, pressure, etc.), their selection criteria, and placement strategies. Data acquisition techniques and considerations.
⢠Data Analysis for Predictive Maintenance: Descriptive, diagnostic, and predictive analytics in PdM. Time-series analysis, statistical process control, and machine learning techniques.
⢠Condition Monitoring Technologies: Vibration analysis, infrared thermography, oil analysis, ultrasound testing, and motor current analysis.
⢠Predictive Maintenance Software and Systems: Overview of software tools and platforms used for data management, analysis, and visualization. Integration with enterprise asset management systems.
⢠Implementation Strategies for Predictive Maintenance: Planning, executing, and monitoring a PdM program. Change management, training, and organizational alignment.
⢠Reliability-Centered Maintenance (RCM): Principles and benefits of RCM. How it supports predictive maintenance and improves overall equipment effectiveness.
⢠Case Studies in Predictive Maintenance: Real-world examples of successful PdM implementations. Examining the impact on maintenance costs, equipment uptime, and safety.
⢠Ethics in Predictive Maintenance: Responsible use of data, transparency, and privacy considerations. Addressing potential biases in predictive models.
⢠Future Trends in Predictive Maintenance: Emerging technologies (Artificial Intelligence, Internet of Things, etc.) and their impact on PdM. The role of predictive maintenance in Industry 4.0 and the smart factory.
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