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In this course, you will learn:
- Conditional probabilities and probability rules
- Continuous function expectation and variance, as well as their adjustment.
- Modeling of failure rates.
- Normal, lognormal, exponential, Weibull, binomial, and Poisson distributions are all examples of distributions.
- Reliability, mean time to failure, and availability are all important factors to consider.
- Data fitting and assessment of reliability
- Mixed multiple failure mechanisms and multimodal distributions
- Block diagrams of reliability.
- Monte Carlo simulation is a technique for simulating events.
- Interference due to load strength and probabilistic design
- Methods of first-order dependability.
- Accelerated testing and acceleration factors are two terms that are often used interchangeably.
- Time to failure modelling in mechanical and electrical systems for chosen failure mechanisms.
Syllabus:
- Introduction and Overview
- Rules of Probability
- Probability Examples
- Conditional Probability
- Expectations and Variance Definition
- Expectation and Variance of Continuous Functions
- Normal Distribution PDF and CDF
- Load-Strength Interference Theory
- Load-Strength Interference Examples
- Material Degradation and Time to Failure Modeling
- Practice Problems for Test 1
- Lognormal Distribution, Reliability, Hazard Rate and MTTF
- Exponential Distribution and Examples of MTTF Estimation
- Weibull Distribution
- Multimodal Distributions and Mixed Multiple Failure Mechanisms
- Goodness of Fit
- Binomial and Poisson Distributions
- Practice Problems for Test 2
- Reliability Block Diagrams
- Monte Carlo Simulation
- Uncertainty in Geometry, Load and Strength
- Covariance and Correlation
- Covariance and Correlation Examples
- First Order Reliability Methods Introduction
- First Order Reliability Methods Examples
- Reliability Review During Design
- Accelerated Degradation
- Accelerated Testing and Acceleration Factors
- Practice Problems for Test 3
- Time to Failure Models for Mechanical Systems
