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samplestream:example [2024/04/24 02:39]
optrix 		samplestream:example [2024/04/25 23:50] (current)
optrix
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 {{cupcakelag.png}} {{cupcakelag.png}}
  
-In this case, we're going to ask for both the **oven temperature** and **final colour** on a cupcake-making machine. These two pieces of equipment are 50m apart from one-another. We want the values synchronised with the conveyor system, compensating for the lag between the two components.+In this case, we're going to ask for both the **oven temperature** and **final colour** on a cupcake-making machine. These two pieces of equipment are 50m apart from one-another.  
 + 
 +===Example Chart=== 
 + 
 +This is what a chart of our temperature and colour values looks like at the moment. Thanks to the distance between the two components, there's a significant amount of lag between the readings from the //oven// compared to the readings from the //inspection station//. 
 + 
 +{{linechartbakerynormal.png}} 
 + 
 +If we wanted to report on cupcake quality and get insight into //why// the quality was low, we can make this much clearer by compensating for the lag. 
 + 
 +===Identify Key Elements=== 
 + 
 +First, we determine the [[source|source of our lag]]. The real source is //distance//. To compensate for the distance between our assets, we need either a //counter// or a //rate// that reflects the distance moved by our conveyor. 
 + 
 +The **speed of the conveyor** is perfect for this - it's a rate of //distance// over //time//. 
 + 
 +Next, we determine our [[end asset|end asset]]. In this case, the last asset in the system is the **Inspection Section**. 
 + 
 +Finally, we want to measure our [[distance|distances]] between each asset and our end asset. From the diagram at the top of the page, we can see that the distance between the Oven and the Inspection Station is **50m**. 
 + 
 +===The Code===
  
 <code python> <code python>
  
-srv = ardiapi.Server('demo.optrix.com.au/s/pl')+srv = ardiapi.Server('cupcake.ardi')
  
 lcq = samplestream.LagCorrectedQuery(srv) lcq = samplestream.LagCorrectedQuery(srv)
-lcq.RateLagQuery('Strip Meters',"'Paint Line' ASSET 'Speed - Actual' PROPERTY VALUES") +lcq.RateLagQuery('Meters',"'Conveyor' ASSET 'Speed' PROPERTY VALUES") 
-lcq.AddQuery("'Finish Oven Zone 1' ASSET 'Temperature - Oven' PROPERTY VALUES") +lcq.AddQuery("'Inspection Station' ASSET 'Brownness' PROPERTY VALUES") 
-lcq.AddQuery("'Prime Oven Zone 1' ASSET 'Temperature - Oven' PROPERTY VALUES",lag=50)+lcq.AddQuery("'Oven' ASSET 'Temperature' PROPERTY VALUES",lag=50)
 lcq.multiplier = 0.0166 lcq.multiplier = 0.0166
 lcq.shavems = True lcq.shavems = True
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 This line tells the class that we are going to correct for lag based on the //Speed// property from our //Conveyor//. This is a **rate of change**, so we'll use the //RateLagQuery// function. This line tells the class that we are going to correct for lag based on the //Speed// property from our //Conveyor//. This is a **rate of change**, so we'll use the //RateLagQuery// function.
  
-   lcq.AddQuery("'Inspection Station' ASSET 'Darkness' PROPERTY VALUES")+   lcq.AddQuery("'Inspection Station' ASSET 'Brownness' PROPERTY VALUES")
  
-This line adds a query for the 'darkness' of our cupcakes, measured at the **Inspection Station**. This station is our [[end asset|end asset]] (the machine at the end of our process), so we won't include a lag for it.+This line adds a query for the 'brownness' of our cupcakes, measured at the **Inspection Station**. This station is our [[end asset|end asset]] (the machine at the end of our process), so we won't include a lag for it.
  
    lcq.AddQuery("'Oven' ASSET 'Temperature' PROPERTY VALUES",lag=50)    lcq.AddQuery("'Oven' ASSET 'Temperature' PROPERTY VALUES",lag=50)
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 We then send a request for the last hour of data. We then send a request for the last hour of data.
  
------+===The Results===
  
 In our results, we get the following... In our results, we get the following...
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 |1.2|62%|132.0| |1.2|62%|132.0|
 |2.4|61%|131.9| |2.4|61%|131.9|
-|3.6|55%|120.2| +|3.6|60%|131.8| 
-|4.8|60%|131.7|+|4.8|55%|120.7|
 |6.0|62%|131.6| |6.0|62%|131.6|
  
-You'll notice that you don't have a //time// index anymore in the resulting dataframe. Instead, you've got a total amount of whatever you're measuring as a //rate// or //total// (in this case, it's meters).+You'll notice that you don't have a //time// index anymore in the resulting dataframe. Instead, you've got a total amount of the [[source|source of lag]] seen (in this case, it's meters of conveyor-belt).
  
 ===What It Means=== ===What It Means===
  
-Each of the values are synchronised - so when **meters** is 0, we're seeing the darkness and the oven temperature for **Cupcake #1**, even though those two points were measured minutes apart.+Reading our original chart, people may //suspect// that there's a direct relationship between the two dips, but it requires technical knowledge and very clean data to be confident. 
 + 
 +But after lag compensation, our chart looks like this... 
 + 
 +{{linechartbakerysync.png}} 
 + 
 +Each of the values are synchronised - so when **meters** is 0, the level of browning and the oven temperature are for **Cupcake #1**, even though those two points were measured minutes apart. 
 + 
 +Looking at the data, you'll see a drop after 4.8m. **Both** the darkness and oven temperature decreased. This makes the correlation extremely obvious.
  
-Looking at the data, you'll see a drop at 3.6m into our data. Both the darkness and oven temperature decreased.+It also helps when dealing with noisy data. Variables are often influenced by a variety of different factors, which makes spotting root-causes difficult. But by correcting for lag, these relationships become **much** clearer, even for people unfamiliar with your system.
  
-If this was a normal query or trend, these two events would appear 5 minutes apart, making it awkward to clearly display that the issues are related.+===Going Further===
  
-But by compensating for the lag between components, our dataframe shows an extremely clear link between the drop in temperature and the quality of our cupcake. +You can extend this even more by using ARDIs relationships and storing the [[distance|distance]] between assets in ARDI itself.
  
 +See an [[adaptable_example|example of an adaptable solution]] for details.

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