I provided some quick feedback on this some time ago on Linkedin and suffice to say, there are some definite benefits to AI performing vibration analysis for well-established indicators for imbalance, alignment, oil whirl, etc... However, anomalies such as corrosion, fatigue, harmonic resonances, and especially process induced problems requires experience or the feel of the deck plates, if you will, to objectively assess risk and provide the intuition for corrective action. Someday, you may be able to simply ask a computer, what's wrong with this turbine but NOT in this generation and probably the next. Seasoned, experienced, vibration specialists will remain a critical resource.to ensure world class reliability for rotating equipment for a long time.
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Victor E Rioli
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Original Message:
Sent: 05-30-2023 13:54
From: kate kerrigan
Subject: As engineer, how do you feel about AI-based Predictive Maintenece?
This post was originally from 2019. Change in the AI world has been rapid. We now have ChatGPT, Bing AI, and more being released every month. I am wondering if you are still in this space Paula, and what is the latest? Personally, I think the idea of AI analyzing machine data is much needed. My worry is how we give it enough background to do it's job? How do we share data without opening access to proprietary information? Many companies still haven't made the switch to keep their machine information and IT systems separate. Until that is complete, we can't even contemplate using AI in an organization. I have used ChatGPT, and it just didn't have enough source material yet, to be useful in reliability. (An example, ChatGPT attributed some of Ricky Smith's work to me; I assume not because it found a link, but because I was asking and Ricky has done some powerful stuff.) When we are talking about vibration data for example, where would the source data come from? Would the software providers have a way to pull that data into their data archives? If so, how do companies opt out and still use the software? (I'm sure Alexa is listening to me, even though I've opted out every way I know how.) We need to have traceability of where information came from and anonymity for provided data. How is this solved in the condition-based monitoring space?
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kate kerrigan
Original Message:
Sent: 10-21-2019 03:30
From: Paula Romero
Subject: As engineer, how do you feel about AI-based Predictive Maintenece?
Hi all,
I am a DSP and data scientist working with solutions to vibration and ultra sound diagnostic using data modelling, machine learning and deep learning. I have been in this industry for a couple of years and it is hard to identify the feelings of the users of our application. Sometimes they seem very suspicious about what the software is saying, other times they feel pretty happy about the results.
I'm worried about it because as I don´t have direct contact with the plants engineers, so that my only source of feedback is what the project manager says. And the PM is also a computer scientist. So, there is some kind of pressure to implement the automation in the plants.
So, this questions if for reliability engeneers around:
- Have you ever had contact with AI solution (or other data-driven approach) for asset maintenence?
- From your experience, what are positive and negative points?
- How do you feel about it? Do you trust it? Do you think it is very imature? Do you think there is no way to reflect engeneer's experience in the models?
- Do you have any suggestions or doubts I can help with?
Thank you very much, people. Any detail, please contact me on:
paularomerolopes@gmail.com