Using the Open Source OPC-UA Client and Server for Your IIoT Solutions | Jeroen Coussement | CEO, Factry
2 likes•2,293 views
The document discusses the use of open-source OPC-UA clients and servers for IIoT solutions by Factry, an industrial software company. It covers the evolution of industrial technology from Industry 1.0 to Industry 4.0, highlighting the challenges in data management and integrating different standards. Additionally, it provides examples of using time-series data and Flux for various industrial applications, such as monitoring bioreactors and energy consumption.
![DEMO FLUX - Pressure drop (2/2)
join(
tables: {pt01:UF1PT01, pt02:UF1PT02},
on: ["_time"]
)
|> map(fn: (r) => ({
_time: r._time,
_pressureDrop: r._value_pt01 - r._value_pt02
}))
|> filter(fn: (r) => r._pressureDrop > 0)
|> aggregateWindow(columns: ["_pressureDrop"], every: 1h, fn: mean, createEmpty: false)](https://image.slidesharecdn.com/usingtheopensourceopc-uaclientandserverforyouriiotsolutionsbyjeroencoussement-190621161338/85/Using-the-Open-Source-OPC-UA-Client-and-Server-for-Your-IIoT-Solutions-Jeroen-Coussement-CEO-Factry-38-320.jpg)
![DEMO FLUX - Filter on status (1/2)
NTU = from(bucket: "historian")
|> range(start: 2019-05-01, stop: 2019-05-02)
|> filter(fn: (r) => r._measurement == "CV-UF1NTU01" and r.status == "Good")
|> aggregateWindow(every: 30s, fn: mean)
|> keep(columns: ["_value", "_time"])
Status = from(bucket: "historian")
|> range(start: 2019-05-01, stop: 2019-05-02)
|> filter(fn: (r) => r._measurement == "Status" and r.status == "Good")
|> map(fn: (r) => ({
_time: r._time,
_status: float(v: contains(value: int(v: r._value), set: [11,21])
}), mergeKey: false)](https://image.slidesharecdn.com/usingtheopensourceopc-uaclientandserverforyouriiotsolutionsbyjeroencoussement-190621161338/85/Using-the-Open-Source-OPC-UA-Client-and-Server-for-Your-IIoT-Solutions-Jeroen-Coussement-CEO-Factry-41-320.jpg)
![DEMO FLUX - Filter on status (2/2)
union(tables: [NTU, Status])
|> sort(columns: ["_time"], desc: false)
|> fill(column: "_status", usePrevious: true)
|> fill(column: "_status", value: 0.0)
|> fill(column: "_value", usePrevious: true) // same for the values themselves
|> fill(column: "_value", value: 0.0)
|> map(fn: (r) => ({_time: r._time, _filteredValue: r._value * r._status}), mergeKey: false)
|> yield()](https://image.slidesharecdn.com/usingtheopensourceopc-uaclientandserverforyouriiotsolutionsbyjeroencoussement-190621161338/85/Using-the-Open-Source-OPC-UA-Client-and-Server-for-Your-IIoT-Solutions-Jeroen-Coussement-CEO-Factry-42-320.jpg)
Using the Open Source OPC-UA Client and Server for Your IIoT Solutions | Jeroen Coussement | CEO, Factry
- 1. Using the Open Source OPC-UA Client and Server for Your IIoT Solutions JEROEN COUSSEMENT - INFLUXDAYS 2019 - LONDON
- 2. ABOUT FACTRY ▸ Based in Ghent, Belgium ▸ Industrial software solutions ▸ Tools for data-driven operational improvement ▸ Factry Historian ▸ Lots of open-source
- 4. INDUSTRY
- 5. INDUSTRY 1.0 STEAM POWER / MECHANISATION
- 7. INDUSTRY 3.0 COMPUTERS & AUTOMATION https://www.flickr.com/photos/greenmambagreenmamba/5307396963/in/photostream/, (CC BY-ND 2.0))
- 8. SUPERVISORY CONTROL SYSTEM (SCADA/DCS) PROGRAMMABLE LOGIC CONTROLLER (PLC) PROCESS SENSORS AND ACTUATORS PROGRAMMABLE LOGIC CONTROLLER (PLC)
- 9. INDUSTRY 4.0 CYBER PHYSICAL SYSTEMS / AI / ML
- 10. A GAP BETWEEN OT AND IT. OT IT?
- 11. SOMETIMES WE HAVE TO GO FULL RETRO VERY LONG LIFECYCLES
- 12. “WE’VE BEEN DOING IT FOR YEARS AND IT WORKS” SLOW ADOPTION
- 13. EVERYONE HAS THEIR OWN STANDARD VENDOR LOCK-IN
- 14. SUPERVISORY CONTROL SYSTEM (SCADA/DCS) PROGRAMMABLE LOGIC CONTROLLER (PLC) PROCESS SENSORS AND ACTUATORS EXTERNAL APPLICATIONS ( Artificial Intelligence / Machine Learning / … ) PROGRAMMABLE LOGIC CONTROLLER (PLC)
- 15. WHAT KIND OF TIME-SERIES DATA DO WE HAVE IN INDUSTRY ?
- 16. DEVOPS DATA vs INDUSTRIAL DATA DevOps Industry Metrics cpu load, disk I/O, database stats, ... temperature, pressure, flow, valveState ... Resolutions seconds to minutes (sub)seconds Retention weeks, then downsampling 5 to 10 years, no downsampling Main goals incident detection, performance monitoring quality guarantee, predictive maintenance
- 17. THE FIRST TIME-SERIES DATABASES
- 19. Process historian refers to a database software application that logs or historizes time-based process data.
- 20. SUPERVISORY CONTROL SYSTEM (SCADA/DCS) PROGRAMMABLE LOGIC CONTROLLER (PLC) PROCESS SENSORS AND ACTUATORS InfluxDB EXTERNAL APPLICATIONS ( Artificial Intelligence / Machine Learning / … ) PROGRAMMABLE LOGIC CONTROLLER (PLC)
- 22. OPC-UA DESCRIPTION ▸ Industrial communication protocol ▸ Successor of OPC - OLE for Process Control ▸ Vendor independent / Platform independent ▸ Released in 2006, but became adopted only recently ▸ Integrated on newest controllers, but retrofit on older ones possible.
- 23. OPC-UA DATA STRUCTURE ▸ Hierarchical data structure ▸ Everything is represented as a data node. ▹ Each node has a unique nodeID ▹ Consists of namespace + identifier ▹ ns=2;s=S7:12_03_12.DB100.4,r
- 24. SUPERVISORY CONTROL SYSTEM (SCADA/DCS) PROGRAMMABLE LOGIC CONTROLLER (PLC) PROCESS SENSORS AND ACTUATORS InfluxDB EXTERNAL APPLICATIONS ( Artificial Intelligence / Machine Learning / … ) PROGRAMMABLE LOGIC CONTROLLER (PLC)
- 25. OPC-UA LOGGER OPEN SOURCE ▸ Reads data from OPCUA sources ▸ Local buffering for no data loss ▸ github.com/coussej/ node-opcua-logger
- 26. OPC-UA LOGGER DEMO ▸ Configure some nodes to be collected from simulation server
- 27. OPC-UA LOGGER OPEN SOURCE ▸ V2 coming soon ▹ Cleaner code (async/await!) ▹ Better buffering ▹ Deploy as a single binary
- 30. USE-CASE: WIND TURBINE OPERATIONS
- 31. SUPERVISORY CONTROL SYSTEM (SCADA/DCS) PROGRAMMABLE LOGIC CONTROLLER (PLC) PROCESS SENSORS AND ACTUATORS InfluxDB EXTERNAL APPLICATIONS ( Artificial Intelligence / Machine Learning / … ) PROGRAMMABLE LOGIC CONTROLLER (PLC)
- 32. OPC-UA SERVER OPEN SOURCE ▸ Return algorithm results back to the process control ▸ Exposing last() value of specified series over OPCUA ▸ github.com/factrylabs/ influx-opcua-server
- 33. OPC-UA SERVER OPEN SOURCE ▸ DEMO
- 34. HOW DOES FLUX FIT INTO THE PICTURE?
- 35. FLUX CUSTOMER QUESTION 1 ▸ Input Pressure ▸ Output Pressure ▸ Can we calculate Pressure drop? ▸ Math across measurements!
- 37. DEMO FLUX - Pressure drop (1/2) UF1PT01 = from(bucket: "historian") |> range(start: 2019-05-01, stop: 2019-05-25) |> filter(fn: (r) => r._measurement == "CV-UF1PT01") |> aggregateWindow(every: 3m, fn: mean, createEmpty: false) UF1PT02 = from(bucket: "historian") |> range(start: 2019-05-01, stop: 2019-05-25) |> filter(fn: (r) => r._measurement == "CV-UF1PT02") |> aggregateWindow(every: 3m, fn: mean, createEmpty: false)
- 38. DEMO FLUX - Pressure drop (2/2) join( tables: {pt01:UF1PT01, pt02:UF1PT02}, on: ["_time"] ) |> map(fn: (r) => ({ _time: r._time, _pressureDrop: r._value_pt01 - r._value_pt02 })) |> filter(fn: (r) => r._pressureDrop > 0) |> aggregateWindow(columns: ["_pressureDrop"], every: 1h, fn: mean, createEmpty: false)
- 39. FLUX CUSTOMER QUESTION 2 ▸ NTU (Nephelometric Turbidity Unit) ▸ Machine Status ▸ Can we see the NTU only when the machine is in production?
- 41. DEMO FLUX - Filter on status (1/2) NTU = from(bucket: "historian") |> range(start: 2019-05-01, stop: 2019-05-02) |> filter(fn: (r) => r._measurement == "CV-UF1NTU01" and r.status == "Good") |> aggregateWindow(every: 30s, fn: mean) |> keep(columns: ["_value", "_time"]) Status = from(bucket: "historian") |> range(start: 2019-05-01, stop: 2019-05-02) |> filter(fn: (r) => r._measurement == "Status" and r.status == "Good") |> map(fn: (r) => ({ _time: r._time, _status: float(v: contains(value: int(v: r._value), set: [11,21]) }), mergeKey: false)
- 42. DEMO FLUX - Filter on status (2/2) union(tables: [NTU, Status]) |> sort(columns: ["_time"], desc: false) |> fill(column: "_status", usePrevious: true) |> fill(column: "_status", value: 0.0) |> fill(column: "_value", usePrevious: true) // same for the values themselves |> fill(column: "_value", value: 0.0) |> map(fn: (r) => ({_time: r._time, _filteredValue: r._value * r._status}), mergeKey: false) |> yield()
- 44. JEROEN COUSSEMENT [email protected] FACTRY OPEN OPERATIONAL INTELLIGENCE