Reliability centered maintenance allows for a cost effective maintenance policy that is focused on a system's different modes of failure and consecuences.Most of the s, the failures of a system are fundamentaly random in their behaviour, thus, having a tool capable of simulating this random behaviour thousands or even millions of s in order to get a statistical trend is extemely valuable so that we can plan maintenance policies that tackle the most likely failing modes and the most catastrophic ones.Monte Carlo methods is an umbrella terms that covers all the studies that rely on many similations of random systems in order to get their most likely behaviour over the span of several tries. I had the opportunity to work this specific topic on my undergraduate thesis ''RAM analysis of electrical power system on the operational context using sequential Monte Carlo'' back in 2016 and got awarded with honors upon my disertation. I'll thrive myself to pour everything I learned into this course. I'm looking forward for your questions and feedback!

Overview
Section 1: Introduction

Lecture 1 Course scope and objectives

Section 2: Basic concepts

Lecture 2 Why Monte Carlo

Lecture 3 Mathematical concepts: Probability density function (PDF).

Lecture 4 Mathematical concepts: CPF and ICPF.

Lecture 5 Practical examples: Random sample generation through ICPFs

Lecture 6 Mathematical concepts: STD, SEM and CI.

Lecture 7 Practical examples: Commands for acquiring STD, SEM and CI.

Lecture 8 Reliability, Availability and Mantainability definitions (TTF, TTR ,MTTF, MTTR)

Lecture 9 Practical examples: Adjusting a sample to a probability density function

Lecture 10 Simulating an individual component's on and off status switches over tmission 1

Lecture 11 Simulating an individual component's on and off status switches over tmission 2

Lecture 12 Creating our first Monte Carlo RAM analysis code for an individual component

Lecture 13 How many simulations are enough

Lecture 14 Exponential probability density function (''memoryless'' function)

Section 3: How do systems fail

Lecture 15 What is a reliability block diagram

Lecture 16 Simulating series and parallel RBDs

Lecture 17 Minimum cut sets (applied to electrical power systems) for RBD construction.

Lecture 18 The adjecency matrix of a graph

Lecture 19 Generating the adjacency matrix of a power system using Matlab

Lecture 20 Practical case: applying the adjacency matrix function to our big case of study.

Lecture 21 Example of elaboration of an RBD for a power system.

Lecture 22 How do systems fail and how to simulate this failings

Section 4: Building our Monte Carlo RAM analysis software

Lecture 23 Simulating a series system

Lecture 24 Simulating a parallel system

Lecture 25 Making sure our Monte Carlo RAM Analysis software works

Lecture 26 Finding the weak links within the RBD

Lecture 27 Final project of section 4 (part I)

Lecture 28 Final project of section 4 (part II)

Section 5: LOLP and LOLE

Lecture 29 Loss of load probability, Loss of load expectation and how to calculate them.

Lecture 30 Applying LOLP and LOLE to build a deteistic MATLAB simulation

Lecture 31 Why Monte Carlo Part I

Lecture 32 Why Monte Carlo Part II

Lecture 33 Explaining a matlab function to calculate LOLE and LOLP

Lecture 34 Creating a Monte Carlo software to calculate LOLP and LOLE

Lecture 35 LOLE and LOLP as a function of the target load

Lecture 36 Evolving LOLP and LOLE for a big generation system

Lecture 37 Wrapping up LOLE and LOLP

Section 6: Wrapping up all

Lecture 38 Wrapping up all

Section 7: Congratulations and course closing

Lecture 39 bye bye

Lecture 40 BONUS

Eeers and eeering students,Matlab practitioners,Reliability centered maintenance eeers,Reliability eeers,Maintenance eeers,Maintenance managers

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