3 ms·

I’m just about to start digging in to 8 years of data from a few power plants with 16 turbines in total to see if I can identify some problems we might have bef

I’m just about to start digging in to 8 years of data from a few power plants with 16 turbines in total to see if I can identify some problems we might have before the sensor measurements exceed the alarm threshold. Taking bearing temperature as an example, I think I will identify periods of time where the machine has already been generating for an hour so temperature have stabilized and then I will have bearing oil inlet temperature and machine load as independent variables, and bearing oil outlet and bearing metal temperatures as dependent values. Seems like it should be straightforward to find any anomalies but I’ve just started googling how to do this yesterday. There are lots of vendors hawking predictive maintenance software but I can’t imagine that I couldn’t get similar results with a few weeks effort and armed with Python and all of the associated libraries

bigger_cheese 3y ago
Maybe try slopes and second derivatives (change in temperature over time and so forth) could also try introducing various lag windows into timeseries data. edit: I've Also seen a lot of pitches about predictive maintenance / automated anomaly detection. I think the appeal lies in having a one size fits all solution you can apply to multiple pieces of equipment (fans, conveyor belt drives, pumps etc) and not needing to develop/deploy/maintain bespoke models. A lot of manufacturing sites won't have a data person on tap (or even people who can write python). Also there are challenges with deployment etc especially in remote sites where access is difficult, data connectivity is bad etc (think like oil/gas pipelines). Most of the pitches seem to combine running ML models and using some kind of iot device with something like lorawan for connectivity..
- applied_heat 3y ago
  Is the product being sold the setup of the bespoke models from their bag of what they’ve done before? Regressions seem like the obvious way to detect anomalies to me since it should be 100% repeatable and make sense according to amount of heat being generated and removed , how to apply ML/AI to it I am not so sure
mr_toad 3y ago
A good first step would be a scatterplots, time series plots and mark 1 eyeballs. It helps to understand the shape of the data before you start trying to fit models.
- applied_heat 3y ago
  Right, make some scatter plots of bearing temp vs oil inlet temp and machine load, establish a fitted line, then can detect anomaly when new measurements vary from expected by more than some threshold. Doesn’t seem that fancy, but better than waiting for a small problem to turn in to a larger problem. I think it will also be useful in highlighting differences between identical machines, why does the one right beside the other run 5 degrees hotter on thrust bearing? etc
nerdponx 3y ago
If you have any kind of functional physical model of part where and eventual failure, you have a huge head start. That said, there can be a pretty big gap between detecting individual sensor anomalies (undergrad homework) and predicting component failure (build an entire business around it). I have never regretted starting a data project with a small, easy task, and ramping up from there. Whereas I have definitely regretted starting a data project with big goals and/or fancy techniques at the beginning. Set clear incremental goals, and use the early prototyping phases to explore the data and develop a good understanding for what might or might not be possible to accomplish with it.
afrnz 3y ago
Having worked in the same problem space, I can heavily recommend to get expert input when evaluating which features to use. Ideally, this is a person who knows the internals of the machinery and/or operations that can help you remove spurious features. As a Data Scientist, one sometimes tends to think that the data explains everything and no expert domain knowledge is needed ("Modern machine translation does work without any knowledge of grammar or language!"). Good luck!

bigger_cheese 3y ago

Maybe try slopes and second derivatives (change in temperature over time and so forth) could also try introducing various lag windows into timeseries data. edit: I've Also seen a lot of pitches about predictive maintenance / automated anomaly detection. I think the appeal lies in having a one size fits all solution you can apply to multiple pieces of equipment (fans, conveyor belt drives, pumps etc) and not needing to develop/deploy/maintain bespoke models. A lot of manufacturing sites won't have a data person on tap (or even people who can write python). Also there are challenges with deployment etc especially in remote sites where access is difficult, data connectivity is bad etc (think like oil/gas pipelines). Most of the pitches seem to combine running ML models and using some kind of iot device with something like lorawan for connectivity..

applied_heat 3y ago
Is the product being sold the setup of the bespoke models from their bag of what they’ve done before? Regressions seem like the obvious way to detect anomalies to me since it should be 100% repeatable and make sense according to amount of heat being generated and removed , how to apply ML/AI to it I am not so sure

mr_toad 3y ago

A good first step would be a scatterplots, time series plots and mark 1 eyeballs. It helps to understand the shape of the data before you start trying to fit models.

applied_heat 3y ago
Right, make some scatter plots of bearing temp vs oil inlet temp and machine load, establish a fitted line, then can detect anomaly when new measurements vary from expected by more than some threshold. Doesn’t seem that fancy, but better than waiting for a small problem to turn in to a larger problem. I think it will also be useful in highlighting differences between identical machines, why does the one right beside the other run 5 degrees hotter on thrust bearing? etc

nerdponx 3y ago

If you have any kind of functional physical model of part where and eventual failure, you have a huge head start. That said, there can be a pretty big gap between detecting individual sensor anomalies (undergrad homework) and predicting component failure (build an entire business around it). I have never regretted starting a data project with a small, easy task, and ramping up from there. Whereas I have definitely regretted starting a data project with big goals and/or fancy techniques at the beginning. Set clear incremental goals, and use the early prototyping phases to explore the data and develop a good understanding for what might or might not be possible to accomplish with it.

afrnz 3y ago

Having worked in the same problem space, I can heavily recommend to get expert input when evaluating which features to use. Ideally, this is a person who knows the internals of the machinery and/or operations that can help you remove spurious features. As a Data Scientist, one sometimes tends to think that the data explains everything and no expert domain knowledge is needed ("Modern machine translation does work without any knowledge of grammar or language!"). Good luck!