• Class and Course

    Risk and Reliability Engineering

    This course provides the basic knowledge and tools to determine the risk and reliability of equipment, a process and a system. It is designed to be 50% theoretical and 50% practical.

    Day 1
    • Introduction
      • Basic Concepts
        • Risk
        • Risk Analysis
        • Reliability
        • Reliability Engineering
        • Uncertainty – Uncertainty Management
        • Asset Life Cycle Economic Analysis
        • Deterministic Model
        • Probabilistic Model
        • Risk-Based Life Cycle Economic Analysis
        • An Integrated Approach to Optimize the Economy of the Asset Life Cycle”
        • Concept, Applications and Benefits
        • Integrated Reliability Toolkit
          • Disciplines
          • Methodologies
    Day 2
    • Statistics for Risk and Reliability Analysis
      • Basic Terms (Probability, Population, Sample, Descriptive Statistics, Random Variable)
      • Descriptive Statistics
      • Sample Statistics
      • Histograms •
      • Statistics (Sample Mean, Sample Mode, Sample Standard Deviation, Percentiles)
      • Using Excel™ for sample statistics
        • Population Statistics
        • Probability Distribution Models
        • Probability Distribution Formats (pdf, CDF, Inverse CDF)
        • Statistics of probability distributions (Mean, Mode, Median,
        • Probabilistic characterization of a sample or How to select a probability distribution model for a given sample.
        • Goodness of fit Tests
        • Use of software for goodness of fit tests. Statistics for Risk and Reliability Analysis
        • Solving mathematical models (equations) random variable input parameters
        • Sampling from a random variable
        • Monte Carlo Simulation
        • Using Excel™ for Monte Carlo Simulation
        • Probabilistic Dependence
        • Correlated Random Variables – Correlation Factor
        • How to consider probabilistic dependence in Monte Carlo Simulation
    Day 3
    • Principles of Production Processes Reliability Analyisis
      • Reliability, Availability and Maintainability (RAM)
      • Analysis for Repairable Items
      • Samples Data for RAM Analysis of repairable items
      • Up-Time Data (Failure Data and Censored Data)
      • Down Time Data
      • Probability Distribution for Up -Time Samples in Repairable Items
      • Probability Distribution for Down-Time Samples in Repairable Items
      • Methodology for Availability and Expected Number of Failures calculations for Repairable items
      • Availability and Expected Number of Failures Calculations
      • Sources of Data for Reliability Analysis
      • Evidence, Expert Opinion, Generic Data • Combining different sources of data
      • Bayes Theorem
      • Combining Generic Data with Evidence
      • Combining Expert Opinion with Evidence
      • System Availability and Expected Number of Failures Calculations
      • Forecasting failures and availability of a pumping system
      • Forecasting failures and availability in an ESP Oil Well
    Day 4
    • Principles of Financial Analysis
      • Basics Terms and Definitions
      • Life time of an investment , Projected Cash Flow, Discount Rate
      • Financial Indicators • Net Present Value (Deterministic Model, Probabilistic Model)
      • Annualized Present Value (Deterministic Model, Probabilistic Model)
    • Principles of Risk Analysis
      • Basic Terms and Definition
      • Undesired Events
      • Basic Model
      • Qualitative and Semi-Quantitative Methods for Risk Analysis
      • Quantitative Risk Analysis (QRA)
      • An ESP Oil Well QRA Example
    Day 5
    • Risk Based Asset Economic Life Cycle Analysis
      • General Model
      • Monte Carlo calculation of NPV
      • Sensitivity Analysis of Probabilistic NPV
      • Determining the Risk Mitigation Actions Portfolio
      • Comparing Investments Options – Risk / Profitability Matrix
      • Final Exercise: Life Cycle Economic Analysis of an ESP Oil Well

    Production engineers, Surface facilities Engineers, Reliability Engineers and Maintenance Engineers

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