drive machine learning to train data
is their data going to be a good predictor to roadmap failures?
generators ended up being way too difficult
only runs for 30 mins with super long idle time
analyze idle time and run time when it’s generating on the load to see if there were any coorelations with the data to figure out if we could build a goo dmodel
30 mins is not good enough
potential multi-step approach to look outside of just the raw data
switchgear
much longer run times so much more data
very few fail instances for switchgear assets
challenging to get good predictive models from their data
even 1 out of 20 failure predictions is worth it - but if it caused more false positives then it creates more work - need to strike the right balance
no point in rushing the machine learning pieces
getting volumes of data from goole was challenging
they didn’t instrument their assets
data quality and governance issues occurred
got the non-ML products looking at health from condition-based data: less risk
pattern: if you have a lot of corrective maintenance on equipment > the asset is likely unhealthy
samples from assets (oil contaminants in an oil transformer)
go-live date on 300 generators in October
once you generate a score in MAS APM, how do you close the loop on service requests and work orders
connect both systems to consistently
send data from Maximo to our APM and back
work order data, thresholds