Webinar - JMP Reliability Platforms – A Zoom In then Zoom Out Introduction

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Sponsor:   IEEE Boston/Providence/New Hampshire Reliability Chapter

                Please visit www.ieee.org/bostonrel

Host:        IEEE Boston/Providence/New Hampshire Reliability Chapter


FREE Webinar

JMP has a comprehensive suite of tools for reliability data analysis and reliability engineering, to address problems from time-to-failure distribution analysis, warranty forecast, accelerated life test data analysis, to degradation data analysis, recurrent events data analysis, reliability growth, and all the way to system reliability analysis using reliability block diagram and system maintenance simulation.

The presenter will start with two live demonstrations. One illustrates the capability of warranty forecast in the Reliability Forecast platform. The other one illustrates the Bayesian modeling capability of analyzing accelerated life test data. The demonstrations also serve as an introduction to JMP in general. After the demonstration, the presenter will go through additional examples or use cases that highlight some of the distinctive capabilities of JMP reliability platforms.

 



  Date and Time

  Location

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This Webinar is to be delivered via Zoom.

At registration, you must provide a valid e-mail address to receive the Webinar Session link approximately 15 hours before the event.  If you haven't received the e-mail as scheduled, please check your spam folder and alternate e-mail accounts before contacting the host.

  • Zoom Webinar

Staticmap?size=250x200&sensor=false&zoom=14&markers=37.3329622%2c 121
  • Michael W. Bannan, Chair

    IEEE Boston/Providence/New Hampshire Reliability Chapter

  • Starts 05 February 2021 06:00 PM
  • Ends 23 February 2021 06:00 PM
  • All times are US/Eastern
  • No Admission Charge
  • Register


  Speakers

Dr. Peng Liu

Dr. Peng Liu of SAS Institute, Inc.

Topic:

JMP Reliability Platforms – A Zoom In then Zoom Out Introduction

JMP has a comprehensive suite of tools for reliability data analysis and reliability engineering, to address problems from time-to-failure distribution analysis, warranty forecast, accelerated life test data analysis, to degradation data analysis, recurrent events data analysis, reliability growth, and all the way to system reliability analysis using reliability block diagram and system maintenance simulation.

The presenter will start with two live demonstrations. One illustrates the capability of warranty forecast in the Reliability Forecast platform. The other one illustrates the Bayesian modeling capability of analyzing accelerated life test data. The demonstrations also serve as an introduction to JMP in general. After the demonstration, the presenter will go through additional examples or use cases that highlight some of the distinctive capabilities of JMP reliability platforms.

Biography:

Peng Liu; JMP Reliability R&D Expert; SAS

Dr. Peng Liu, JMP's advanced statistician and software engineer, holds bachelor’s degrees for industrial foreign trade and computer science, and a master’s and Ph.D. in statistics. At JMP, Liu is mainly responsible for developing and maintaining software for multiple fields, including all platforms for reliability and survival data analysis, reliability engineering, and time sequence analysis. He also studies the methods of reliability data analysis and engineering, time-sequence analysis, man-machine interface design, and the architecture and management of large software systems. Dr. Liu is also an active researcher on reliability data analysis and reliability engineering. Over the years, he has accumulated a great deal of experience in helping users solve practical problems.





Agenda

 

11:00 AM   Technical Presentation

11:45 AM   Questions and Answers

12:00 PM   Adjournment



The meeting is open to all.  You do not need to belong to the IEEE to attend this event; however, we welcome your consideration of IEEE membership as a career enhancing technical affiliation.

There is no cost to register or attend, but registration is required.