Simulation of MTBF with controls F(t) = 1 - e ^ -λt  Where   • F(t) is the probability of failure   • λ is the failure rate in 1/time unit (1/h, for example)  • t is the observed service life (h, for example) The inverse curve is the trust time On the right the increase in failures brings its
Science Strategy Failure Risk MTBF BIFURCATIONS Growth Population Demographics Navier Stokes Engineering TURBULENCE Chaos Fractals Health Biology Physics Mathematics Finance Economics Environment
Simulation of MTBF with controls

F(t) = 1 - e ^ -λt 
Where  
• F(t) is the probability of failure  
• λ is the failure rate in 1/time unit (1/h, for example) 
• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life
The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

Wearout
The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period. 
Clone of Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK
Ivan Stamenkovic
Tischtennisball aus 5m Höhe mit und ohne Strömungswiderstand.
Physics
Tischtennisball aus 5m Höhe mit und ohne Strömungswiderstand.
Fall mit Luftreibung
Wolfgang Thomaser
Insight diagram
Physics
Kette rutscht über eine Kante
Matthias Meßmer
Le but de cette simulation est de montrer comment on peut utiliser un circuit hydraulique à effet de seuil (en l'occurrence un mécanisme de siphon) pour implémenter des fonctions logiques basées sur des flux hydrauliques Nous allons utiliser ces fonctions pour construire un circuit arithmétique
Physics logic
Le but de cette simulation est de montrer comment on peut utiliser un circuit hydraulique à effet de seuil (en l'occurrence un mécanisme de siphon) pour implémenter des fonctions logiques basées sur des flux hydrauliques

Nous allons utiliser ces fonctions pour construire un circuit arithmétique capable de calculer en binaire la somme de deux nombres de 3 bits chacun, ce qui donne un résultat sur 4 bits.

Le circuit est basé sur un assemblage tout à fait classique de briques élémentaires, à savoir un demi-additionneur suivi de 2 additionneurs. pleins. Au cœur du circuit, on trouve une porte logique un peu particulière, capable de calculer en même temps le XOR et le AND des deux entrées. La retenue est obtenue en OR-ant la sortie des deux portes AND, dont le flux doit être réduit en introduisant un réservoir intermédiaire qui permet de diviser le flux en 2 parties égales.
Le résultat sur chaque bit est utilisé pour activer (par l'intermédiaire d'une action) un état en sortie.

Nous n'avons malheureusement pas réussi à changer la couleur de l'état en fonction de sa valeur. Celle-ci peut être changée dans le panneau de configuration, afin de tester des valeurs différentes.

On notera que pour obtenir un résultat correct, deux conditions sont nécessaires:
1°) Il faut attendre que les flux dans les portes se stabilisent, ce qui prend pas moins de 10 secondes (et qui reflète le délai de latence inhérent à tout circuit, qui correspond environ au nombre maximum de portes logiques traversées entre l'entrée et la sortie du circuit.
2°) Il faut utiliser la méthode de simulation basée sur une approximation de Runge-Kutta, sous peine de voir apparaître des oscillations parasites dans certaines portes qui rendent le résultat instable.


L'eaurdinateur
Jean-Daniel NICOLET
5 7 months ago
Problem of the sliding chain
Mathematics Cegep Physics
Problem of the sliding chain
Clone of Sliding Chain
Felix
Connects distance, velocity, and acceleration.
Physics
Connects distance, velocity, and acceleration.
DVA metric
Edythe
Insight diagram
Physics Scientific Modelling
Problema da queda de pedras de granizo: modelo 1, sem resistência
Milena Lauschner Lopes
Insight diagram
Agriculture Physics Fluid Mecanics Cleen Energy
Clone of Bomba de golpe de Ariete 10 10 2021
Rocio Hernandez
Insight diagram
Motion Physics
Clone of Pendulum
Sebastian Zander
​The probability density function (PDF) of the normal distribution or Bell Curve of Normal or Gaussian Distribution is the mean or expectation of the distribution (and also its median and mode).   The parameter is its standard deviation with its variance then, A random variable with a Gaussi
Physics Intelligence Weather Climate Ecology Science Education Probability Density Function Normal Bell Statistics Curve Gaussian Distribution Democracy Voting Politics Policy MATHS
​The probability density function (PDF) of the normal distribution or Bell Curve of Normal or Gaussian Distribution is the mean or expectation of the distribution (and also its median and mode). 

The parameter is its standard deviation with its variance then, A random variable with a Gaussian distribution is said to be normally distributed and is called a normal deviate.
However, those who enjoy upskirts are called deviants and have a variable distribution :) 

A random variable with a Gaussian distribution is said to be normally distributed and is called a normal deviate.

If mu = 0 and sigma = 1

If the Higher Education Numbers Are Increased then the group decision making ability of society would be raised above that of a middle teenager as it is now
BUT 
Governments can control children by using bad parenting techniques, pandering to the pleasure principle, so they will make higher education more and more difficult as they are doing


85% of the population has a qualification level equal or below a 12th grader, 17 year old ... the chance of finding someone with any sense is low (~1 in 6) and the outcome of them being chosen by those who are uneducated in the policies they are to decide is even more rare !!!

Experience means little if you don't have enough brain to analyse it

Democracy is only as good as the ability of the voters to FULLY understand the implications of the policies on which they vote., both context and the various perspectives.   National voting of unqualified voters on specific policy issues is the sign of corrupt manipulation.

Democracy:  Where a group allows the decision ability of a teenager to decide on a choice of mis-representatives who are unqualified to make judgement on social policies that affect the lives of millions.
The kind of children who would vote for King Kong who can hold a girl in one hand and swat fighter jets out of teh sky off the tallest building, doesn't have a brain cell or thought to call his own but has a nice smile and offers little girls sweets.


updated 16/3/2020 from 4 years 3 months ago
Clone of ​The probability density function (PDF) of the normal distribution or Bell Curve Gaussian Distribution by Guy Lakeman
Kyra Evers
7 months ago
Rotating Pendulum Z201 from System Zoo 1 p80-83
Science System Zoo Education Physics

Rotating Pendulum Z201 from System Zoo 1 p80-83

Clone of Rotating Pendulum
Chenwei Gui
9 months ago
A PID control loop for a simple linear system Some stochasticity in the throttle and sensor ​
Physics Control
A PID control loop for a simple linear system
Some stochasticity in the throttle and sensor ​
1st Order PID Loop
Nicholas Milford
3
THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES TURBULENT CHAOTIC DESTRUCTION The existing global capitalistic growth paradigm is totally flawed Growth in supply and productivity is a summation of variables as is demand ... when the link between them is broken by catastrophic failure in a compon
TURBULENCE Environment Economics Finance Chaos Health Biology Physics Fractals Engineering Mathematics Strategy Demand Supply Navier Stokes
THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES TURBULENT CHAOTIC DESTRUCTION

The existing global capitalistic growth paradigm is totally flawed

Growth in supply and productivity is a summation of variables as is demand ... when the link between them is broken by catastrophic failure in a component the creation of unpredictable chaotic turbulence puts the controls ito a situation that will never return the system to its initial conditions as it is STIC system (Lorenz)

The chaotic turbulence is the result of the concept of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite working containers (villages communities)

Clone of THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES CHAOTIC TURBULENCE (+controls)
Nhat_Minh DOAN
Problem of the sliding chain
Physics Cegep Mathematics
Problem of the sliding chain
Clone of Sliding Chain
Marc-Alexandre Fortin
Insight diagram
Wind Resistance Kinematics IACS Physics
Rocket Model 2015 DiCarlo
Christopher DiCarlo
22
Insight diagram
Physics
Styrofoam Ball Dynamics Model
Marc
Potential energy to Velocity
Physics Education
Potential energy to Velocity
Clone of Velocity
Filipe Fernandes de Paula
Simple example of a 1D falling object, comparing the use of direct equations, with "solving" the differential equation using flows (dQ/dt) and stocks (Q).
Physics
Simple example of a 1D falling object, comparing the use of direct equations, with "solving" the differential equation using flows (dQ/dt) and stocks (Q).
Clone of 1D Falling Object
Wellink
Insight diagram
Physics
6. Raket met brandstofverbruik
Fysica
3
Insight diagram
Physics
James Linskey rocket model tuned to match
James Linskey
Simulation of MTBF with controls F(t) = 1 - e ^ -λt  Where   • F(t) is the probability of failure   • λ is the failure rate in 1/time unit (1/h, for example)  • t is the observed service life (h, for example) The inverse curve is the trust time On the right the increase in failures brings its
Chaos Environment Economics Strategy Failure Risk MTBF BIFURCATIONS Finance Growth Mathematics Population Demographics Science Physics Biology Navier Stokes Engineering Health TURBULENCE Fractals
Simulation of MTBF with controls

F(t) = 1 - e ^ -λt 
Where  
• F(t) is the probability of failure  
• λ is the failure rate in 1/time unit (1/h, for example) 
• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life
The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

Wearout
The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period. 
Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK
atif
Simulation der Umlaufbahn der Erde um die Sonne
Physics
Simulation der Umlaufbahn der Erde um die Sonne
Clone of Kepler Ellipsen
LeaBla
Insight diagram
Physics
E2a. Optrekken met luchtwrijving
Fysica
17
Simulation of MTBF with controls F(t) = 1 - e ^ -λt  Where   • F(t) is the probability of failure   • λ is the failure rate in 1/time unit (1/h, for example)  • t is the observed service life (h, for example) The inverse curve is the trust time On the right the increase in failures brings its
TURBULENCE Demographics Fractals Physics Science Failure BIFURCATIONS Strategy Growth Risk Economics Navier Stokes Finance Engineering Biology Chaos Mathematics Environment Health Population MTBF
Simulation of MTBF with controls

F(t) = 1 - e ^ -λt 
Where  
• F(t) is the probability of failure  
• λ is the failure rate in 1/time unit (1/h, for example) 
• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life
The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

Wearout
The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period. 
Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK
Ariel Ortiz Gomes