Automobile Warranty Data Analysis by Anita Rebarchak DSES-6070 HV5 Statistical Methods for Reliability Engineering Professor Ernesto Gutierrez-Miravete.

Slides:



Advertisements
Similar presentations
FMECA & Reliability Analysis of an Elastomeric Rotating Scissor Bearing of Helicopter Jacques Virasak RPI DSES-6070 HV5 Statistical Methods for Reliability.
Advertisements

Optimization of Cutter Life DSES 6070 HV5 Professor Gutierrez-Miravete By: Frank Gibilisco.
MODULE 2: WARRANTY COST ANALYSIS Professor D.N.P. Murthy The University of Queensland Brisbane, Australia.
8. Failure Rate Prediction Reliable System Design 2011 by: Amir M. Rahmani.
Stats for Engineers Lecture 11. Acceptance Sampling Summary One stage plan: can use table to find number of samples and criterion Two stage plan: more.
GoldSim 2006 User Conference Slide 1 Vancouver, B.C. The Submodel Element.
Understanding the Accuracy of Assembly Variation Analysis Methods ADCATS 2000 Robert Cvetko June 2000.
Helicopter System Reliability Analysis Statistical Methods for Reliability Engineering Mark Andersen.
Department of Electrical Engineering National Chung Cheng University, Taiwan IEEE ICHQP 10, 2002, Rio de Janeiro NCCU Gary W. Chang Paulo F. Ribeiro Department.
BRIDGING THE GAP BETWEEN THEORY AND PRACTICE IN MAINTENANCE D.N.P. (Pra) MURTHY RESEARCH PROFESSOR THE UNIVERSITY OF QUEENSLAND.
Reliability A. A. Elimam. Reliability: Definition The ability of a product to perform its intended function over a period of time and under prescribed.
Tensile Strength of Composite Fibers Author: Brian Russell Date: December 4, 2008 SMRE - Reliability Project.
Tensile Strength of Composite Fibers Author: Brian Russell Date: November 20, 2008 SMRE - Reliability Project.
Coating Cans Wear Analysis Kevin Jacob DSES_6070.
1 Fundamentals of Reliability Engineering and Applications Dr. E. A. Elsayed Department of Industrial and Systems Engineering Rutgers University
Introduction Before… Next…
Copyright © 2014 reliability solutions all rights reserved Reliability Solutions Seminar Managing and Improving Reliability 2014 Agenda Martin Shaw – Reliability.
Warranty Forecasting of Electronic Boards using Short- term Field Data Mustafa Altun, PhD Assistant Professor Istanbul Technical University
Statistical Methods in Reliability Diesel Generator Fan Failure Data Jesse Lesperance Spring 2008.
CARLOS CEDEÑO DSES /04/2008 Reliability of the Three Main Engines of Space Shuttle.
Reliability of Automated Insulin Pumps Medtronic Mini-Med Paradigm® DSES-6070 HV7 Statistical Methods for Reliability Engineering Professor Ernesto Gutierrez-Miravete.
Computer Simulation A Laboratory to Evaluate “What-if” Questions.
Reliability Model for Compressor Failure SMRE Term Project Paul Zamjohn August 2008.
1 Reliability Prediction A Quest for Reliable Parameters By Yair Shai.
©Ian Sommerville 2004Software Engineering, 7th edition. Chapter 24 Slide 1 Critical Systems Validation 1.
On Model Validation Techniques Alex Karagrigoriou University of Cyprus "Quality - Theory and Practice”, ORT Braude College of Engineering, Karmiel, May.
Monte Carlo Simulation and Personal Finance Jacob Foley.
Presentation Ling Zhang Date: Framework of the method 1 Using Distribution Fitting for Assumptions 2 monte – carlo simulation 3 compare different.
12-CRS-0106 REVISED 8 FEB 2013 Data Analytics in Electronics Manufacturing IEEE NSW Section Stefan Mozar.
Integrated circuit failure times in hours during stress test David Swanick DSES-6070 HV5 Statistical Methods for Reliability Engineering Summer 2008 Professor.
1 6. Reliability computations Objectives Learn how to compute reliability of a component given the probability distributions on the stress,S, and the strength,
MAT 4830 Mathematical Modeling 05 Mean Time Between Failures
Copyright © 2014 reliability solutions all rights reserved Reliability Solutions Seminar Managing and Improving Reliability 2015 Agenda Martin Shaw – Reliability.
Reliability Analysis of a Ti-35II Calculator Rensselaer Polytechnic Institute Department of Engineering and Science DSES-6070 HV6 Statistical Methods for.
Rensselaer Polytechnic Institute Lally School of Management & Technology MGMT 6070 Statistical Methods for Reliability Engineering Term Project Presentation.
Example (which tire lasts longer?) To determine whether a new steel-belted radial tire lasts longer than a current model, the manufacturer designs the.
DSES6070 Statistical Methods for Reliability Engineering Reliability of a Bicycle as a Commuter Vehicle By Adam Brown.
1 Reliability Evaluation of Trailer Axles -Larry McLean Final Project for DESE-6070HV7 Statistical Methods for Reliability Engineering Dr. Ernesto Gutierrez-Miravete.
A Different Type of Monte Carlo Simulation Jake Blanchard Spring 2010 Uncertainty Analysis for Engineers1.
Aircraft Windshield Failures Statistical Methods for Reliability Engineering Professor Gutierrez-Miravete Erica Siegel December 4, 2008.
EML EML 4550: Engineering Design Methods Probability and Statistics in Engineering Design: Reliability Class Notes Hyman: Chapter 5.
Reliability Reliability of Adhesives used in High Quality Audio Components.
Shanghai Jiao Tong University 1 ME250: Statistics & Probability ME 250: Design & Manufacturing I School of Mechanical Engineering.
1 3. System reliability Objectives Learn the definitions of a component and a system from a reliability perspective Be able to calculate reliability of.
Machine Design Under Uncertainty. Outline Uncertainty in mechanical components Why consider uncertainty Basics of uncertainty Uncertainty analysis for.
Investigation of Turbine Electric Motor Winding Failure Rate Data DSES-6070 HV3 and HV4 Statistical Methods for Reliability Engineering Fall 2007 Michael.
Bayesian Travel Time Reliability
Reliability of New High Tech Roller Coasters By: Topher Schott DSES-6070 HV5 Statistical Methods for Reliability Engineering Spring 2008 Professor Ernesto.
1 1 Slide Simulation Professor Ahmadi. 2 2 Slide Simulation Chapter Outline n Computer Simulation n Simulation Modeling n Random Variables and Pseudo-Random.
Components are existing in ONE of TWO STATES: 1 WORKING STATE with probability R 0 FAILURE STATE with probability F R+F = 1 RELIABLEFAILURE F R Selecting.
Determination of the Optimal Replacement Age for a Preventive Maintenance Problem involving a Weibull Failure Probability Distribution Function Ernesto.
Increased Reliability Through Failure Predictive Scheduling with Temperature Sensor Feedback Wesley Emeneker CSE 534 Dr. Sandeep Gupta.
Mean Time To Repair
Rick Walker Evaluation of Out-of-Tolerance Risk 1 Evaluation of Out-of-Tolerance Risk in Measuring and Test Equipment Rick Walker Fluke - Hart Scientific.
 How do you know how long your design is going to last?  Is there any way we can predict how long it will work?  Why do Reliability Engineers get paid.
© 2016 Minitab, Inc. Reliability for Your Company's Survival Bonnie Stone, Minitab October 19,
Maintenance strategies
Martin Shaw – Reliability Solutions
MORE SOLVED EXAMPLES Lecture 5 Prof. Dr. Ahmed Farouk Abdul Moneim.
An EXCEL Add-In for Comparing Two Exponential Distributions
8.1 Normal Approximations
Martin Shaw – Reliability Solutions
Introduction to Probability & Statistics Inverse Functions
Estimating probability of failure
Martin Shaw – Reliability Solutions
Project-X Status Report
Introduction to Probability & Statistics Inverse Functions
Production and Operations Management
RELIABILITY Reliability is -
Presentation transcript:

Automobile Warranty Data Analysis by Anita Rebarchak DSES-6070 HV5 Statistical Methods for Reliability Engineering Professor Ernesto Gutierrez-Miravete 8/11/2008

Objective Use warranty data collected by the manufacturer for 329 cars of the same make, model and year with a 40,000 km warranty Predict engine failure Forecast future warranty costs

Data Observations: Engine is very reliable. Most failures were very minor and accumulated no significant cost. Data only lists the first failure per engine; additional failures my have occurred over the life of the warranty

Limitations Delay in reporting failure overstates the lifetime of the failed component Data does not give component details Data does not account for multiple failures in an engine Engine is very complex and the data does not account for components relying on each other Driving environment not considered

Conclusions Wear out

Additional Analysis Normal distribution calculations in Maple Monte Carlo Simulation for MTTF Censored analysis including all 329 engines Cost analysis at MTTF