Reliability Engineering Statistics
What you'll learn
- Reliability engineering probability and statistics
- How to use Excel for reliability calculations
- Continuous distributions like normal, lognormal, exponential, and Weibull
- Discrete distributions like binomial, poisson, geometric and hypergeometric
- Reliability block diagrams (series, parallel, and k of n configurations)
- Process capability analysis (Cp, Pp, Cpk and Ppk)
- Building and interpreting control charts
- Definitions for common reliability terms
- Modality, skew and kurtosis
- Confidence intervals
- Preparation for ASQ's Certified Reliability Engineering exam
- Basic knowledge of Microsoft Excel
- Basic knowledge of statistics and engineering
- Analytical mindset
"Reliability Engineering Statistics" was designed by reliability and quality engineering professionals to teach the foundational analytical skills needed to advance your career in reliability engineering.
Based on the American Society of Quality's (ASQ) Body of Knowledge for the Certified Reliability Engineer (CRE) exam , this course steps through each of the Basic Concepts found in the section titled "Probability and Statistics for Reliability". But this is far more than an exam prep course!! Each concept is supplemented with real-world examples, "how to" sections in Microsoft Excel, and detailed explanations that unpack the theories into a genuinely useful set of analytical tools.
In this course, you will learn the statistical skills needed to continue onto more advanced topics like accelerated life testing and reliability planning. The topics in this course include:
Continuous distributions (normal, lognormal, exponential, Weibull and chi square)
Discrete distributions (binomial, poisson, geometric, hypergeometric)
Working with distributions in Microsoft Excel
Permutations, combinations, Venn diagrams
Building and interpreting control charts
Process capability analysis (Cp, Cpk, Pp, Ppk)
Evaluating reliability block diagrams (series, parallel, k of n)
Modality, kurtosis, skew and MUCH MORE!!
While not a full exam prep course, "Reliability Engineering Statistics" will demystify the toughest and longest section (over 25%) of the CRE exam, and teach you the concepts you need to understand higher level concepts like modeling accelerated life test and warranty failure data.
This class will take your analytical problem solving skills through the roof and prepare you to start tackling the toughest problems in engineering and design.
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Who this course is for:
- Reliability engineers
- Quality engineers
- Electrical engineers
- Engineering and technical managers
- Mechanical engineers
- Product and process designers
- Continuous improvement professionals
- Six sigma black belts
Ray Harkins is a senior manufacturing professional with 30 years experience in manufacturing engineering, quality management, and business analysis. During his career, he has toured hundreds of manufacturing facilities and worked with leading industry professionals throughout North America and Japan.
He earned his Bachelor of Science from the University of Akron where he majored in Engineering Technology, his Master of Science from Rochester Institute of Technology where he majored in Manufacturing Leadership and Project Management, and is about to complete his Master of Business Administration from Youngstown State University.
He is a senior member of the American Society of Quality, and holds their Quality Engineering (CQE), Quality Technician (CQT), Quality Auditing (CQA) and Calibration Technician (CCT) certifications.
Ray has written extensively for national trade publications on the topics of quality engineering and career management, and has taught nearly 30,000 students through the Udemy platform on a range of manufacturing-related topics.
I am a seasoned ASQ certified reliability engineer (CRE) with experience in life data analysis, reliability modeling and simulation, warranty analysis and life predictions.
My work has been with both military and commercial products and I have experience in product development as well as sustaining existing products.
I specialize in using existing data to predict the future so management can make good decisions regarding warranty costs, inventory requirements, expected part replacements, etc.