[cgio]

Predictive maintenance is not that controversial. I built that for a big mining company with pretty broad asset base about 10 years ago. It’s easy to plug some ML in the place of other prediction models. The challenge with these programs are important nuances such as work order definitions and standardisation, whether you use new vs refurbished components, impact on cost, downstream maintenance impacts, useful life etc. Quite exciting domain, I wish I worked on that a bit more as I hand to hand over and people who received got confused on even more basic staff such as dynamic updates on strategy, I.e. run to failure vs predictive etc. Therefore, yes, it can possibly go wrong, but no, it does not have to.

[baz00]

Yes this. 25 years ago we had a big engineering management system and ran statistical analysis on assembly level MTTF and MTTR. This analysis would be fed back into maintenance scheduling and recall notification. Result interpretation after post mortem would drive design changes, testing and supplier contracts.

As for AI, I suspect this is just another excuse to use it over more formal methods. I prefer the formal methods for various reasons I can't be bothered to list here.

[philberto]

Do you mind sharing a couple of reasons?

[baz00]

1. Difficult to reason how it came to a conclusion which is required for evidencing spending.

2. Cost

[bilekas]

Yes. In my previous job we used to do this for pharma manufacturing grid and their engineers were always able to identify the same markers or flags that needed to be addressed. I have no doubt they're looking to automate more of that work as much as possible.

With minor manual intervention to confirm etc you're still saving engineers so much time. Would have been a really nice upsell for my last company too.