Thorsten Vogt

Hi, the error means that you are referencing a variable that is not defined in your variable list. You should change your variable "$signal1" in the formula to a variable you have in your variables list: Also be aware that you cannot use a signal and a condition together on combineWith(). You can combine either signals only or conditions only. Regards, Thorsten

Hello Kspring, I think you can do it that way: First create a condition based on the material signal by using toCondition(). The function creates a capsule everytime the value of the signal changes and stores the value in a defined property, in this case "Material": $signal.toCondition('Material') Next filter only for batches where the current material is produced using formula: //Create capsule of 1 day ending now $capsule = capsule(now()-1d, now()) //Get current material by selecting last value inside capsule created above $currentMaterial = $gradeCode.toScalars($capsule).last() //Only keep batches for current Material $batches.removeLongerThan(1mo).keep($c -> $c.property('Material') == $currentMaterial) Then create the condition the time range you want to aggregate over. Here I am using a monthly condition: Last step is calculating the value. The within() function is used to only keep that portions of the signal during the specified condition. $temperature.within($filteredBatches).aggregate(average(), $month, middleKey()).toDiscrete() Depending on your data you might need to do some adjustments on the timeranges I used in the formulas. Regards, Thorsten

Hi Bellas, for creating the line at a certain y-value you can click on the "f(x)" button: Then click on "New Formula": Enter the formula for the trend line. In this case I am going to display the line at y=60: After executing click the "f(x)" button again and select the just created signal from the dropdown: The scatter plot now shows the line: Coloring an area above that line is not possible as far as I know. But you can color all points above that line. First you need to create a condition that identifies all points of the y-axis signal above the threshold: Next, click on the "Color" button: Select the condition from the conditions list: After closing the dialog all points identified by this condition are colored in the specific color of the condition: ? Regards, Thorsten

Hi Robin, you can create a batch condition by using replace() to extract the batchnumber and toCondition() for creating the capsules for each batch: $subbatches.replace('/(\\d{1,7})NR\\d{3}/', '$1').toCondition() In the next step you can do the aggregation: $v1.aggregate(sum(), $batch.removeLongerThan(1wk), middleKey()) + $v2.aggregate(sum(), $batch.removeLongerThan(1wk), middleKey()) Regards, Thorsten

Hello, you can use the merge() function for this. If a tolerance value is specified all capules beginning withing that tolerance after the end of a capsule are combined. Regards, Thorsten

Hello, you can find deviations by using Value Search. The former Deviation Search Tool has been unified with Value Search in Version R50: https://support.seeq.com/space/KB/1075216533/What's+New+in+R50#Unify-Deviation-Search-and-Value-Search Regards, Thorsten

Hi Javad, I got a solution based on this starting point: The following formula compares the durations of the first capsules for conditions B,C and D bounded by each capsule of condition A and sets a property on capsules of condition A indicating if these should be shown. The keep function on the end only displays capsules where the property is set to true $a.transform($a_capsule -> { $b_capsule = $b.toGroup($a_capsule).first() $c_capsule = $c.toGroup($a_capsule).first() $d_capsule = $d.toGroup($a_capsule).first() $show_a = ($b_capsule.duration() > $d_capsule.duration() and $d_capsule.duration() > $c_capsule.duration()) $a_capsule.setProperty('show', $show_a) }, 40h).keep('show', isequalTo(true) ) Changing capsule duration of B gets no capsule for result, which is expected: If you want to show another capsule for the last case, you can adjust the formula to generate another condition with the property show set to the inverse and select one of the conditions based on that value. Therefore you have to do the transform twice: $tempCondition1 = $a.transform($a_capsule -> { $b_capsule = $b.toGroup($a_capsule).first() $c_capsule = $c.toGroup($a_capsule).first() $d_capsule = $d.toGroup($a_capsule).first() $show_a = ($b_capsule.duration() > $d_capsule.duration() and $d_capsule.duration() > $c_capsule.duration()) $a_capsule.setProperty('show', $show_a) }, 40h) $tempCondition2 = $tempCondition1.transform($a_capsule -> { $d_capsule = $d.toGroup($a_capsule).first() $d_capsule.setProperty('show', not $a_capsule.property('show')) }, 40h) $tempCondition1.keep('show', isequalTo(true)) or $tempCondition2.keep('show', isequalTo(true)) As a result capsules for condition D are shown: Changing duration of capsule B to its original value show capsules of A as the result: Hope this helps. Regards, Thorsten

Hi Matthias, you can try this: First, calculate the duration of each capsule using "Signal from Condition" tool: Then you can use timesince() to calculate the percentage value over the duration and also splice() to insert it into the base signal of 0% everytime the condition is met: 0%.splice(timesince($condition.removeLongerThan(40h), 1min)/$duration, $condition) Did I understand your question correctly? Regards, Thorsten

Hi Yassine, maybe this post is helpful: Regards, Thorsten

Hi Rohan, you can use the replace() function for this: $signal.replace('/(.{22}).*/', '$1') The snippet takes the first 22 characters and replaces the original value of the signal with the characters captured. Regards, Thorsten

Hi Kenny, you can do this by first calculating the total duration of each capsule using Signal from Condition Tool: Here "Discrete" is used as the interpolation method as interpolated values of the calculated duration between the capsules for further calculations are not needed. Next you can calculate the average duration again using Signal from Condtion: Depending on where you put the value in step 1 the calculated average can change. In the foregoing example I put the value to the start of the capsule therefore only capsules starting in the bounding condition are used for calculation. If necessary, further adjustments must be made with regard to your usecase. Regards, Thorsten

Hi Dylan, you can use the aggregate function for this: $signal.aggregate(average(), ($signal > 90).removeLongerThan(1wk), durationKey()) In this example the average of the signal is calculated whenever its value is above 90 and the result is drawn as a line over the duration of each capsule. You can find more information on the function and its parameters inside formula documentation. Hope this helps. Regards, Thorsten

Hi Yassine, it might be that there are invalid values in this timeframe. You can try to change your formula to $as32.validValues(). This will remove invalid values from the signal and interpolation should work again. If the gap is greater than the max interpolation setting you can use $as32.validValues().setMaxInterpolation(4weeks) which first removes invalid values and then changes the max interpolation of the signal. Regards, Thorsten

Hello Bayu, you may try it this way. I set up a sample in which my forecasted value will exceed the threshold two times in the next 3 days: First step is identifying these periods by using the following formula: ($forecast > $limit).afterStart(0ns) This will create capsules with zero duration as I just need the timestamps for the points in time when the value will be exceed the limit. Next step is creating a new signal with the timestamp as the value: //Iterate over all capsules to create a signal //with the value of the timestamp at start of the capsule $limitExceeded.toSamples($c -> { //Get date of capsule start $date = $c.startKey() //Create sample for signal sample($c.startKey(), $date) }, 1s) Last step is creating the scorecard: In case you may want to display the timestamp in another format you may read this post and adjust the formula in step 2. Hope this helps. Regards, Thorsten

Hi Ben, another option is using the function highpassfilter() as in the following example: $signal.highPassFilter(20min, 2min, 60).abs().agileFilter(2min) The resulting signal can then be used in Value Search to determine the deviations. Regards, Thorsten

Hi Rosa, have you tried keeping just the calculated signal in the list of signals used for the export? Using the OData Feed URL for Excel (I do not have Power BI) just exports 30 values. Regards, Thorsten

Hi Pat, you may use within() fo this. The following example keeps only the portions of the signal where the value of the signal is above 65: Regards, Thorsten

Hi Ingrid, you try to use splice on a scalar, which is available since R51. In lesser versions you can apply splice only on signals, so changing $h to a signal first should resolve the issue: $h.tosignal().splice($l, $lfc) Regards, Thorsten

Hi Aaron, you can use a formula for this. The following example calculates the total duration of all capsules of the operation condition since 06 Mar 2021 up to now. The result is a scalar. $operation.aggregate(totalDuration('h'), capsule('2021-03-06T00:00:00Z', now())) Another option would be creating a condition that can be used in the "Signal from Condition" tool. You have to select an appropriate maximum capsule duration for the condition: condition(30d, capsule('2021-03-06T00:00:00Z', now())) A more dynamic way would be calculating the duration based on the last maintenance. The following formula creates a condition for the times your equipment is running, in case you only have the maintenance capsules. growEnd(30d) extends the capsules to 30 days so you can use it for the current time. As growEnd() extends the capsules to the beginning of the next capsule I use subtract() to remove the maintenance periods from it. $maintenance.inverse().growend(30d).subtract($maintenance) The resulting condition is used inside Signal from Condition Tool: Will this work for you? Regards, Thorsten

In this case you may change the aggregation function from totalized() to delta().

Hi Robin, you can use toCondition() on the Product ID Signal. This will create a condition with capsules each time the value changes. Then you can use Signal from Condition to get the desired values for each capsule. In the following example I calculate the used energy during each compressor stage: Regards, Thorsten

Hi Vladimir, you can add another boundary to the signal. When choosing "Other" as the relation type a dashed line will be added to the display. In case you only want to have the dashed line for the range where the limits are equal you can create another signal for that and chosse this as the boundary: Will this work for you? Regards, Thorsten

Hi Stephanie, yes, there a multiple solutions. I guess the easiest one is using the shifts() function: //Create shift at 05:30 and combine with shift at 17:30 shifts(5.5, 12h).combineWith(shifts(17.5, 12h)) You could also use periods together with move() or a startdate of the initial shift //Create a 12h capsule every 12hs and move to 05:30 / 17:30 periods(12h, 12h).move(5.5h) //Create a 12h capsules every 12hs based on the specified date periods(12h,12h, '2021-01-01T05:30:00+01:00') In order to create just two capsules based on the current date you may use the following formula. As it uses now() the capsules have an uncertain state. //Get timestamp for today and yesterday at midnight $today = now().floorTime(1d) $yesterday = $today - 1d //create capsules condition(12h, capsule($yesterday + 5.5h, $yesterday + 17.5h), capsule($yesterday + 17.5h, $today + 5.5h)) This is what it looks like in Workbench Regards, Thorsten

The API shown above returns the data in JSON format. To create a CSV file you - can write some custom code making use of the Seeq SDKs (here Python) import seeq_sdk as sdk import csv # login prompt definition def get_authenticated_client(api_url): api_client = sdk.ApiClient(api_url) auth_api = sdk.AuthApi(api_client) auth_input = sdk.AuthInputV1() auth_input.username = <username> auth_input.password = <password> auth_input.auth_provider_class = "Auth" auth_input.auth_provider_id = "Seeq" auth_api.login(body=auth_input) return api_client seeq_api_url = 'http://seeqserver:34216/api' seeq_api_client = get_authenticated_client(seeq_api_url) datasourcesApi = sdk.DatasourcesApi(seeq_api_client) output = datasourcesApi.get_items_hosted_by_datasource(id=<id of datasource>, limit=300) with open("C:\\temp\\items.csv", "w", newline='') as csvFile: writer = csv.writer(csvFile) writer.writerow(["Name", "Type"]) for item in output.items: writer.writerow([item.name, item.type]) - take the output from the response body and use 3rd-party tools to convert to csv - use Seeq Data Lab (requires Seeq Data Lab license) data = spy.search({"Datasource Name": "Example Data"}) data.to_csv('items.csv') Regards, Thorsten

Hi Vegini, I guess there are two ways to accomplish this. First one is using Seeq CLI using the command seeq datasource items on the Seeq server. As you can see below you are able to filter for specific datasources you are interested in and also export the results to a .csv file. To get the name, class or id of a datasource you can execute seeq datasource list first to get the desired information to be used for filtering: More information on the CLI can be found here: https://support.seeq.com/space/KB/215384254/Seeq%20Server%20Command%20Line%20Interface%20(CLI) The second way would be utilizing the API, for example in a python script. As you see you have to specify the ID of the datasource to get the associated items. The ID of a datasource can be determined by calling the datasources endpoint: Hope this helps. Regards, Thorsten