Presentation is loading. Please wait.

Presentation is loading. Please wait.

Reliability Model for Compressor Failure SMRE Term Project Paul Zamjohn August 2008.

Published byBrian Merritt Modified over 8 years ago

Similar presentations

Presentation on theme: "Reliability Model for Compressor Failure SMRE Term Project Paul Zamjohn August 2008."— Presentation transcript:

1 Reliability Model for Compressor Failure SMRE Term Project Paul Zamjohn August 2008

2 Proposal Compressor Failure Data: Case 2.16 of Blischke-DATA Data on “large air compressors” for a military base near the seacoast will be analyzed to determine the probabilistic failure structure. Air compressors require “bleeding” prior to operation to function properly, the data below represents failure due to binding in the bleed system. Salt air due to proximity to the ocean is believed to be a major contributor, nothing is known about other variables and their impact to reliability. Analysis will include: Generating the descriptive statistics Selecting the distribution that best describes the data and the distribution parameters Calculating the failure probability density function (f) Calculate the cumulative distribution function (F) Calculating the survival probability function (R) Calculating the hazard function (z) Determining the MTTF Perform Monte Carlo simulation to model and assess reliability

3 Compressor Failure Data

4

5 Failure vs. Reliability Function Probability Distribution Function Hazard (failure) RateMonte Carlo Simulation vs. Equation

Download ppt "Reliability Model for Compressor Failure SMRE Term Project Paul Zamjohn August 2008."

Similar presentations

Time to failure Probability, Survival,  the Hazard rate, and the Conditional Failure Probability.

Time to failure Probability, Survival,  the Hazard rate, and the Conditional Failure Probability.
The Normal Distribution

The Normal Distribution
Probabilistic models Jouni Tuomisto THL. Outline Deterministic models with probabilistic parameters Hierarchical Bayesian models Bayesian belief nets.

Probabilistic models Jouni Tuomisto THL. Outline Deterministic models with probabilistic parameters Hierarchical Bayesian models Bayesian belief nets.
E(X 2 ) = Var (X) = E(X 2 ) – [E(X)] 2 E(X) = The Mean and Variance of a Continuous Random Variable In order to calculate the mean or expected value of.

E(X 2 ) = Var (X) = E(X 2 ) – [E(X)] 2 E(X) = The Mean and Variance of a Continuous Random Variable In order to calculate the mean or expected value of.
On time invariant probabilistic modelling of duration of load effects for timber Sven Thelandersson Structural Engineering Lund University.

On time invariant probabilistic modelling of duration of load effects for timber Sven Thelandersson Structural Engineering Lund University.
Sensitivity Analysis In deterministic analysis, single fixed values (typically, mean values) of representative samples or strength parameters or slope.

Sensitivity Analysis In deterministic analysis, single fixed values (typically, mean values) of representative samples or strength parameters or slope.
Mitigating Risk of Out-of-Specification Results During Stability Testing of Biopharmaceutical Products Jeff Gardner Principal Consultant 36 th Annual Midwest.

Mitigating Risk of Out-of-Specification Results During Stability Testing of Biopharmaceutical Products Jeff Gardner Principal Consultant 36 th Annual Midwest.
Approaches to Data Acquisition The LCA depends upon data acquisition Qualitative vs. Quantitative –While some quantitative analysis is appropriate, inappropriate.

Approaches to Data Acquisition The LCA depends upon data acquisition Qualitative vs. Quantitative –While some quantitative analysis is appropriate, inappropriate.
©GoldSim Technology Group LLC., 2004 Probabilistic Simulation “Uncertainty is a sign of humility, and humility is just the ability or the willingness to.

©GoldSim Technology Group LLC., 2004 Probabilistic Simulation “Uncertainty is a sign of humility, and humility is just the ability or the willingness to.
Helicopter System Reliability Analysis Statistical Methods for Reliability Engineering Mark Andersen.

Helicopter System Reliability Analysis Statistical Methods for Reliability Engineering Mark Andersen.
Relationship Between Sample Data and Population Values You will encounter many situations in business where a sample will be taken from a population, and.

Relationship Between Sample Data and Population Values You will encounter many situations in business where a sample will be taken from a population, and.
Descriptive statistics Experiment  Data  Sample Statistics Experiment  Data  Sample Statistics Sample mean Sample mean Sample variance Sample variance.

Descriptive statistics Experiment  Data  Sample Statistics Experiment  Data  Sample Statistics Sample mean Sample mean Sample variance Sample variance.
Monte Carlo Simulation and Risk Analysis James F. Wright, Ph.D.

Monte Carlo Simulation and Risk Analysis James F. Wright, Ph.D.
Samples vs. Distributions Distributions: Discrete Random Variable Distributions: Continuous Random Variable Another Situation: Sample of Data.

Samples vs. Distributions Distributions: Discrete Random Variable Distributions: Continuous Random Variable Another Situation: Sample of Data.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Continuous random variables Uniform and Normal distribution (Sec. 3.1, 3.4.6-3.4.7)

CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Continuous random variables Uniform and Normal distribution (Sec. 3.1, )
Introduction to Simulation. What is simulation? A simulation is the imitation of the operation of a real-world system over time. It involves the generation.

Introduction to Simulation. What is simulation? A simulation is the imitation of the operation of a real-world system over time. It involves the generation.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Exponential distribution Reliability and failure rate (Sec. 3.2-3.3)

CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Exponential distribution Reliability and failure rate (Sec )
Tensile Strength of Composite Fibers Author: Brian Russell Date: December 4, 2008 SMRE - Reliability Project.

Tensile Strength of Composite Fibers Author: Brian Russell Date: December 4, 2008 SMRE - Reliability Project.
Chapter 14 Simulation. Monte Carlo Process Statistical Analysis of Simulation Results Verification of the Simulation Model Computer Simulation with Excel.

Chapter 14 Simulation. Monte Carlo Process Statistical Analysis of Simulation Results Verification of the Simulation Model Computer Simulation with Excel.
Tensile Strength of Composite Fibers Author: Brian Russell Date: November 20, 2008 SMRE - Reliability Project.

Tensile Strength of Composite Fibers Author: Brian Russell Date: November 20, 2008 SMRE - Reliability Project.

Similar presentations

Ads by Google