Conference on Modelling With Analysis and Prediction of Plant Reliability

Overview:

Introduction:

This conference provides participants with advanced knowledge in plant reliability, focusing on modeling, analysis, and prediction techniques. Through it,  participants will learn how to prevent equipment failures, optimize maintenance schedules, and integrate reliability analysis into decision-making processes for improved plant performance.

Conference Objectives:

By the end of this conference, participants will be able to:

• Understand key principles of plant reliability and their impact on operations.

• Apply reliability modeling techniques to predict failures and enhance system reliability.

• Conduct in-depth reliability analysis using industry-standard tools.

• Implement predictive maintenance strategies based on reliability data.

• Integrate reliability assessments into decision-making for continuous improvement.

Target Audience:

• Reliability Engineers.

• Plant Managers.

• Maintenance Engineers.

• Operations Managers.

• Asset Management Professionals.

Conference Outline:

Unit 1:

Fundamentals of Plant Reliability:

• Introduction to plant reliability concepts.

• Key reliability metrics: MTBF, MTTR, and system availability.

• Importance of reliability in maintenance and operational performance.

• Identifying failure modes in plant systems.

• Relationship between reliability and overall plant efficiency.

Unit 2:

Modeling Techniques for Plant Reliability:

• Introduction to reliability block diagrams (RBD).

• Applying Failure Modes, Effects, and Criticality Analysis (FMECA).

• Using simulation tools for reliability modeling.

• Quantifying reliability with predictive models.

• Creating actionable insights from reliability models.

Unit 3:

Advanced Reliability Analysis Methods:

• Using Weibull analysis for life data analysis.

• Fault tree analysis (FTA) for identifying critical failure points.

• Data collection methods for reliability analysis.

• Interpreting reliability data for decision-making.

• Techniques for reliability audits and performance assessments.

Unit 4:

Predictive Maintenance and Reliability-Based Decision Making:

• Developing predictive maintenance strategies using reliability data.

• Leveraging IoT and sensor technology for real-time monitoring.

• Scheduling maintenance based on reliability predictions.

• Analyzing predictive maintenance success factors.

• Case study: Successful implementation of predictive maintenance in an industrial plant.

Unit 5:

Continuous Improvement and Refining Reliability Models:

• Reviewing and updating reliability models based on performance data.

• Incorporating feedback loops for continuous improvement.

• Identifying long-term reliability improvement opportunities.

• Ensuring alignment between reliability goals and plant operations.

• Creating a roadmap for sustained plant reliability.