Advanced Data Analytics for Process Industry

Introduction: 

This advanced course is designed to equip professionals in the process industry with the skills to leverage data analytics for optimizing maintenance and reliability strategies. Participants will gain a deep understanding of data-driven techniques and their application in predicting equipment failures, improving asset performance, and reducing downtime.

Course Objectives:      

• Master advanced data analytics techniques relevant to process industries.
• Develop a data-driven mindset for decision-making in maintenance and reliability.
• Apply predictive modeling and machine learning algorithms to optimize asset performance.
• Utilize data analytics to identify root causes of equipment failures.
• Implement data-driven strategies for improving overall equipment effectiveness (OEE).

Course Content:

Day 1: Introduction to Advanced Data Analytics and its Application in Process Industries

• Understanding Big Data: Characteristics, sources, and challenges in process industries
• Data Preprocessing and Cleaning: Techniques for handling missing values, outliers, and inconsistencies
• Data Visualization: Effective data visualization tools and techniques for process data
• Statistical Analysis Basics: Descriptive statistics, probability distributions, hypothesis testing

Day 2: Predictive Modeling and Machine Learning Techniques

• Regression Analysis: Linear and nonlinear regression models for predicting equipment performance
• Time Series Analysis: Forecasting equipment failures and performance trends
• Classification Models: Identifying equipment failure modes and root causes
• Clustering Algorithms: Grouping similar equipment or process conditions for analysis

Day 3: Advanced Analytics for Maintenance Optimization

• Prescriptive Analytics: Optimizing maintenance schedules and resource allocation
• Reliability-Centered Maintenance (RCM) and Data Analytics: Integrating data analytics for RCM
• Prognostics and Health Management: Using data to predict equipment failures
• Digital Twin Technology: Creating virtual representations of physical assets for predictive maintenance

Day 4: Maintenance Data

• Equipment data: failure data, method of detection and maintenance activity
• Failure mode and effect analysis
• Root cause failure
• OEE
• Predictive Model Development: Building predictive models for equipment failure prediction
• Data Visualization and Storytelling: Creating compelling visualizations to communicate insights

Day 5: Advanced Topics and Future Trends

• Artificial Intelligence and Machine Learning in Maintenance: Exploring the potential of AI and ML in predictive maintenance
• IoT and Big Data Integration: Leveraging IoT data for advanced analytics
• Digital Transformation and Industry 4.0: The role of data analytics in the digitalization of process industries
• Ethical Considerations in Data Analytics: Privacy, security, and bias in data-driven decision making
• Case study for control data collection separator

Who Should Attend:

• Maintenance and reliability engineers
• Data analysts and scientists in process industries
• Operations managers and supervisors
• Asset management professionals
• Engineers and technicians working in process plants