The Math of Random Signals, Fatigue Damage, Vibration Tests


The Math of Reliability Engineering, Fatigue Damage, and its Interesting Applications in the World of Vibration Tests.

What you will learn

How to define Fatigue Damage mathematically

How to find the probability density of the peaks of a random signal

The importance of the Gaussian distribution and why it often appears when dealing with random signals

How to find the joint probability density of two Gaussian variables

What the Fatigue Damage Spectrum is, and why it is important for Fatigue tests

How to synthesize signals with a prescribed Fatigue Damage Spectrum

Description

Ready to Learn About the Fascinating World of Fatigue Damage and Its Application in Engineering ?

1: Introduction

Are you interested in learning about fatigue damage and its application in engineering? If so, enroll in this comprehensive course and delve deep into the mathematics of fatigue damage, from the derivation of essential equations to the application of the theory in real-life situations.

2: Course Objectives

This course is designed for anyone interested in vibration tests and reliability engineering, as well as those who want to see the beauty of mathematics applied to engineering. You will learn about stochastic processes, probability distributions, and their role in real-life applications, as well as single-degree-of-freedom systems, the probability density of the maxima of a random process, and much more.

3: Instructor Expertise

The concepts in this course are based on the instructor’s PhD dissertation: β€œAdvanced Mission Synthesis Algorithms for Vibration-based Accelerated Life-testing,” which has been publicly available since February 2023 after a two-year “embargo”, because the algorithms contained therein, could not be disclosed.

You will also have access to Graphical User Interfaces (GUI) developed by the instructor in collaboration with companies interested in the PhD project to see how equations can be implemented to generate vibratory signals.

4: Course Structure

This course will introduce you to fatigue-life estimation tests that aim to reproduce the entire fatigue damage experienced by the component during its operational life, but in a shorter amount of time. You will learn about how to tailor these tests to specific applications and generate signals that have the same “damage potential” as the environmental conditions to which the components are subjected.

5: Benefits of the Course

By enrolling in this course, you will gain advanced mathematical concepts that are intuitive to understand, and the connection to real-life applications will always be highlighted. You will learn how to relate a Power Spectral Density (PSD) to the fatigue damage, which is essential for creating signals to be used during tests. Additionally, you will learn about non-Gaussian signals, which are important since there are signals measured in real-life applications that show a deviation from the Gaussian distribution due to the presence of peaks and bursts in the signals.

6: Course Content

The course content includes:

– Introduction to fatigue damage

– Stochastic processes and probability distributions


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– Single-degree-of-freedom systems

– Probability density of the maxima of a random process

– Tailoring fatigue-life estimation tests to specific applications

– Generating signals with the same “damage potential” as environmental conditions

– Relating Power Spectral Density (PSD) to fatigue damage

– Creating Gaussian and non-Gaussian signals

7: Course Delivery

This course is available online and can be accessed at any time, from anywhere in the world. You will have access to instructional videos, but also reading materials (my PhD dissertation), to deepen your understanding if necessary.

8: Course Duration

The course duration is flexible and self-paced, allowing you to learn at your own pace. It is estimated that the course will take around 10-12 hours to complete ( even though it lasts less, but you need time to think !!! πŸ™‚ )

9: Course Completion

Upon completion of this course, you will receive a certificate of completion, which can be used to demonstrate your knowledge and skills in the field of fatigue damage and its application in vibration testing.

10: Who is this course for?

This course is for anyone interested in learning about fatigue damage and its application in engineering, including engineering students, professionals, and anyone with a love for mathematics and its applications.

Enroll today and start exploring the fascinating world of fatigue damage!

English
language

Content

The mathematics of Fatigue Damage

Introduction to the course
Joint Gaussian Distribution
Probability Distribution of the Envelope of a Gaussian Signal (Rayleigh distrib)
Mathematical Definition of Fatigue damage
Formula for the Expected number of Positive Peaks
Power Spectral Density, Standard Deviation of a Random Process & its Derivatives
Relating the Fatigue Damage Spectrum to the Power Spectral Density
Practical Explanation on How to Calculate the Fatigue Damage Spectrum
Maximum Response Spectrum

Applications of the theory of Fatigue Damage

Practical use of the theory of Fatigue Damage
Algorithms for the Calculation of the Power Spectral Density
How to Create Non-Gaussian Signals with a Prescribed Fatigue Damage Spectrum
Application: Synthesis of Non-Gaussian Signals with a Prescribed Fatigue Damage
Application of the Maximum Response Spectrum

How the distribution of a Gaussian signal or sinusoidal signal occurs in nature

How the Central Limit Theorem Arises from Stochastic Processes
Integral of Even Powers of Sinusoids
Some Representations of the Bessel function of Order Zero
Probability Density of a Sinusoid
Distribution of a Sinusoid Derived Intuitively
Why a Random Signal with Uniformly Distributed Phases is Gaussian

Sine on Random Vibrations

Probability Density of the Envelope of Gaussian Noise + a Deterministic Sinusoid
Calculating the Damage caused by Sine on Random Signals

Appendix on Fourier series and Fourier Transforms

Introduction to Stochastic Processes
Derivation of the Properties of a Linear System
Properties of the Fourier Transform of a Real Time Signal
Fourier Series Representation of the Output of a Linear System
Another Useful Representation of the Fourier Series
Recalling the Relationship Between the Fourier Series and the Fourier Transform

Appendix on Ordinary Differential Equations

Solution to 2nd order ODE (ordinary differential equations)

Appendix on Random Variables and the Central Limit Theorem

Sterling’s Formula
How the Binomial Distribution Converges to the Gaussian One