Machine learning and repair

#21

A few quick thoughts post-conf:

In nearly every session the phrase " Your model is only as good as your data" was uttered.

The only real work surrounding ML/AI is being done in the area of engineering - “inside the box”, everything “outside the box” (pre/post modelling) - e.g. design, planning, assurance - is barely acknowledged in either business or academia.

A general air of optimism pervaded the sessions “this WILL improve life” although most of the speakers were engineers who have nothing to do with the outcomes their work will bring.

Dr Lorien Pratt was the best speaker. Too much info to summarise but note-to-self to read all of her references starting with https://www.responsibleaidi.org/

In a nutshell:

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#22

Hmm… They are my credentials. I wonder if the hosting service is blocking you because it thinks your access pattern is different. Can you try in incognito browser mode?

#23

Hello, I’ve been keeping a little eye on the ML for repair research. Here’s an article that describes the possibilities. In my experience these systems are only being used in large scale equipment or infrastructure deployment. Continuous monitoring data (e.g. from vibration sensors) is analysed by AI / ML systems to determine at what life stage the equipment is at. As these types of systems usually involve some sort of predictive maintenance already (based on estimated time to failure rates) AI / ML systems deliver a greater degree of accuracy, therefore in theory saving money as redundancy is reduced. I’ve met other academics working in this area in the mining industry where the equipment is really big and difficult to reach.

It’s intriguing to think about how these systems might apply to everyday objects. In theory this is all possible in the amazing IoT revolution (along with everything else). But at the moment IoT don’t seem to last long enough to truly wear out, and bricking due to manufacturer action (or inaction) is still much more likely. It’s exciting to read about the “Shazam for products” you’re developing @Wouter_Sterkens.

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#24

I like a lot the idea of “Shazam for broken stuff” Thanks Janet this will definitely help to explain the RepairApp we envisage and that we are developing in the framework of the SmartRe project (only in Dutch https://vlaanderen-circulair.be/nl/doeners-in-vlaanderen/detail/smart-re ) in our research group (https://www.mech.kuleuven.be/en/research/LCE). In first instance we use AI to extract information from an image to be able to identify the product model (similar to how Shazam extracts information out of a noisy recording). If we think to know the product model we can search with this info for a best match in databases of past experiences. The next step I see as most valuable is to be able to not only find past experiences for exactly the same product, but for similar products, e.g. from the same series, as more information will be available at the level of a product series. Within the Interreg SHAREPAIR project we plan to continue working on this!

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#25

this is clearly far away from being a real application, but i love the idea, & for professional repairers of phones etc it’d be highly relevant & possibly cost saving.

But as our repair cafe is only 3 events old & yet already i can see a blockage in our system to get repairs assessed & dealt with efficiently & quickly, having already turned some electrical repairs away.

However as some of the participants want ‘‘an experience’’ on the day of the cafe, so time is not their greatest worry but getting it fixed certainly is.

Just a comment for others to mull over

BW Steve

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#26

Dear All,
I am new to the question, but deeeply involved in datascience,
I have spent some few hours on the dataset to compute the embryon of a usefull too to motivate people to go for a repair: an app that answers the questions “can my coffee machine be repaired?”,

see the full article here: https://www.linkedin.com/pulse/my-coffee-machine-dead-exploring-reparability-home-goods-jean-milpied/

with best regards

Jean

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#27

Thanks @JEAN_MILPIED - I’ll link to your post on the topic for more detailed discussion: Can you predict reparability?

#28

Hello, I’ve been keeping a touch eye on the Predictive maintenance Machine Learning. Here’s a piece of writing 5 that describes the chances . In my experience these systems are only getting used in large scale equipment or infrastructure deployment. Continuous monitoring data (e.g. from vibration sensors) is analysed by AI / ML systems to work out at what life stage the equipment is at. As these sorts of systems usually involve some kind of predictive maintenance already (based on estimated time to failure rates) AI / ML systems deliver a greater degree of accuracy, therefore in theory saving money as redundancy is reduced. I’ve met other academics working during this area within the mining industry where the equipment is basically big and difficult to succeed in .

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