Principal Components Analysis for Early Events Detection in Process Operations

  • Type:
    Conference Presentation
  • Skill Level:
    Intermediate
  • PDHs:
    0.50

Share This Post:

You will be able to download and print a certificate for these PDH credits once the content has been viewed. If you have already viewed this content, please click here to login.

Principal Components Analysis (PCA) is a statistical technique that aims at reducing the problem dimensionality by progressively eliminating redundancy in data. The method has found extensive use in exploratory data analysis as it aids visualization of complex data. Interestingly, the method is data driven which makes it suitable for mechanical learning.

Rotating equipment such as pumps, compressors and turbines are vital equipment in processing plants. Malfunction(s) in the operation of such equipment can have serious and sometimes disastrous consequences. Operation of rotating equipment therefore demands continuous surveillance. However, the current state of affairs is less than satisfactory and would greatly benefit from an easy to implement Early Events Detection (EED) program.

We recently investigated one such a possibility of using PCA for EED; the results are promising and are discussed in this paper.

This recording is FREE to members of the Fuels and Petrochemicals Division of AIChE. AIChE members may join the Division for only $10. Join F&PD now and return to this page with your new log-in and you will receive this presentation for FREE.
F&PD Members: Simply click on “click here to buy this archived webcast ” below and your ‘0’ price will appear in your shopping cart.

Checkout

Checkout

Do you already own this?

Log In for instructions on accessing this content.

AIChE MEMBERS

AIChE Member Credits 0.5
AIChE Members $15.00
AIChE Fuels and Petrochemicals Division Members Free
AIChE Undergraduate Student Members Free
AIChE Graduate Student Members Free
Non-Members $25.00