Reliability Engineering and Analysis for Space Missions – Online Short Course 3 March 2027 - 14 April 2027 Online

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Reliability Engineering and Analysis for Space Missions – Online Short Course


  • From 3 March–14 April 2027 (6 Weeks, 6 Classes, 12 Classroom Hours, approximately 24 Total Hours)
  • Every Wednesday from 1–3 p.m. Eastern Time (all sessions will be recorded and available for replay; course notes will be available for download)
  • In this new essential course, offered in partnership with Johns Hopkins University, develop the analytical skills to assess reliability, characterize uncertainty, and quantify risk in complex space systems and missions.
  • All students will receive an AIAA Certificate of Completion and an additional Badge of Completion from Johns Hopkins University at the end of the course.

OVERVIEW

Reliability is critical to the success of complex space systems, where failures can have significant mission consequences and opportunities for repair may be limited. This course introduces the principles, methods, and analytical tools used to evaluate reliability and risk in space systems and missions.

Participants will develop a practical foundation in component and system reliability analysis, including probability and statistical methods, life data analysis, Failure Mode and Effects Analysis (FMEA), reliability block diagrams, fault tree analysis, and event tree analysis. The course also addresses Bayesian data analysis, quantitative reliability modeling, uncertainty propagation, physics-of-failure approaches, and probabilistic risk assessment.

These methods and techniques are brought together to examine reliability and risk at the component, system, and mission levels, including hardware, software, and human reliability and their interdependencies.

LEARNING OBJECTIVES

  • By the end of the course, participants will have a structured framework for selecting and applying appropriate reliability and risk-analysis methods to complex space systems.
  • Construct and interpret various reliability models of ground, launch, and space systems.
  • Identify potential failures and weak points in space system designs.
  • Determine failure model parameters from failure and anomaly data.
  • Augment failure data with expert opinion using Bayesian data analysis techniques.
  • Quantify the probability of failure for engineered systems.
  • Communicate uncertainties of analysis in a quantifiable and traceable manner.

AUDIENCE
Designed for engineers, analysts, technical managers, and other aerospace professionals who need to understand, conduct, interpret, or use reliability and risk analyses for space systems and missions.

COURSE FEES (Register on Johns Hopkins website here) Registration closes on 10 March
– AIAA Member Price: $1595 USD
– Non-Member Price: $1795 USD

CLASSROOM HOURS / CEUs: 12 classroom hours / 2.4 CEU/PDH

CANCELLATION POLICY: A refund less a $50.00 cancellation fee will be assessed for all cancellations made in writing prior to 5 days before the start of the event. After that time, no refunds will be provided.

CONTACT:  Please contact Lisa Le or Customer Service if you have any questions about the course or group discounts.

Frequently Asked Questions

Additional AIAA/Johns Hopkins Courses: In Spring 2027, AIAA and Johns Hopkins University are jointly offering several courses focused on Space Systems Engineering. Those students wishing to deepen their knowledge may be interested the opportunity to participate in more than one of these courses:

Outline
  • Module 1 – Introduction and Background
    • What is reliability engineering? Principles and terminology
    • History – Evolution of reliability engineering and its application to space systems
    • Sources and mechanisms of space-system failure
  • Module 2 – Data Analysis for Components
    • Statistics and probability distributions
    • Determine distribution parameters and quantify associated uncertainties
    • Life data analysis to evaluate failures over time
    • Bayesian approaches to reliability data analysis allowing for the inclusion of multiple forms of information to augment failure data
  • Module 3 –System Analysis (Qualitative)
    • Failure Modes and Effects Analysis(FMEA)
    • Boolean Logic
    • Reliability Block Diagrams (RBDs)
    • Fault Tree Analysis (FTA)
    • Event Tree Analysis (ETA)
    • Selecting an appropriate modeling approach
  • Module 4 –System Analysis (Quantitative)
    • Quantification for reliability models to provide insight to system level risk drivers
    • Propagation of uncertainty in data, parameters, and knowledge through the models
    • Enhancing system models to dependent failures, common cause failure, state models, and pre-cursor analysis
  • Module 5 – Physics of Failure
    • Enhancing traditional reliability analysis
    • Discuss the concept of calculating reliability using physics models of failure mechanisms
    • Explain Stress-Strength, Damage-Endurance, Performance-Requirement Interference models. Given distributions of each calculate the reliability with each relationship
    • Mechanisms of fatigue, Creep, Wear for mechanical system as well as electromigration and dielectric breakdown for electronic systems
  • Module 6 – Probabilistic Risk Assessment (PRA)
    • Quantifying risk of engineered systems
    • Explain the uses for PRA and the differences with traditional reliability analysis
    • Explain the elements that make up a PRA
    • Develop a PRA model for a system
    • Communicate PRA results in the context of uncertainty derived from the model
    • Optional homework exercises are provided to foster a practical understanding of class material. Real world examples and models will demonstrate the techniques and methods shown with a view toward pulling insights from the analyses.
Materials

COURSE DELIVERY AND MATERIALS

  • The course lectures will be delivered via Zoom. Access to the Johns Hopkins learning platform will be provided to registrants near to the course start date.
  • All recorded classes will be available on-demand within 1-2 business days of the lecture. Once available, you can stream the replay video anytime, 24/7.
  • All slides will be available for download after each lecture. No part of these materials may be reproduced, distributed, or transmitted, unless for course participants. All rights reserved.
  • Live virtual sessions are recorded and may be repurposed as course materials at the discretion of Johns Hopkins University (JHU) and viewed by future learners. Recordings may include your name, image, voice, questions, comments, and other participation. Although JHU will make reasonable efforts to remove or minimize personally identifiable information during editing, some identifying information and participation may remain in the recordings.
  • Between lectures during the course, the instructor will be available via email for technical questions and comments.
Instructor

Dr. Clayton Smith

Over 40 years of experience analyzing systems from risk, reliability, and safety perspectives. These systems included: NASA and DoD spaceflight missions, payloads, missile systems, submarine systems, ground communication systems, air traffic control systems, large integrated computer systems, and petro-chemical processing plants. Dr. Smith leads the Reliability and Probabilistic Risk group within the Space Exploration Sector of the Johns Hopkins University – Applied Physics Lab. He created and managed NASA’s International Space Station Program Probabilistic Risk Assessment specifically geared toward quantifying the safety risk during operations. Conducted many safety and reliability analyses that include: Hazard Analyses (Hardware & Operations), Fault Tree Analysis, Availability modeling (Hardware & Software), Failure Modes and Effects Analysis, RF link availability due to rain attenuation, requirement compliance, trade studies for alternate designs, spares provisioning validation, and failure data trending.

EDUCATION

  • PhD, Reliability Engineering, University of Maryland, College Park, MD. 2011.
  • MS, Technical Management, University of Maryland University College, College Park, MD. 1994.
  • BS, Aerospace Engineering, University of Maryland, College Park, MD. 1986.

 

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