1. CERN openlab technical workshop
Siemens collaboration
5th Nov 2015
Filippo Tilaro,
EN/ICE CERN

2. Analytical framework for the CERN control system
Scalable and fault-tolerant !!!
Data Analysis Framework
Expert
Data Processing Modules
Fieldbus TN
PLCs
Sensors
&
Actuators
MOON
(Monitoring)
High Voltage
DIM/CMW
OPC
Field layer
Process layer
Supervision layer
FFT
Neural Network
(R)
+600 Application Services
Analysis memory and configuration
Machine
Learning
CEP
(Java)
Visualisation
Patterns
(LabView)
(WatchCAT)
PLCs
+ 650 FECs
Data collection & feedback
Historical
Data
+50M channels
5th Nov 2015
Siemens CERN openlab

3. Analytical areas of interest
Online monitoring
Continuous service to analyse the system status and inform operators in case of fault detection
Fault diagnosis
“Forensics” analysis of system faults that have already happened in the past. In some cases root-cause analysis
Engineering design
Analysis of historical data to draw conclusions about system behaviours which could be helpful to improve / optimize the system under analysis
Oscillation analysis for cryogenics valves
Anomaly detection by sensors data mining
PID supervision
5th Nov 2015
Siemens CERN openlab

4. LHC Cryogenics system and valve oscillation
Keep magnets under Superconductivity
Liquid helium bathing the LHC’s magnets cooled down to 1.9K
34000 physical instrumentations and channels
12136 AI, 4856 AO,4536 DI,1568 DO
4000 analogical control loops
More than 120 PLCs
Siemens S DP
30000 conceptual objects/parameters
Valve oscillation can affect:
Control system stability
Maintenance (stress on the equipment)
Performances & Safety
Increase in the data load
Wrong valve oscillation
5th Nov 2015
Siemens CERN openlab

5. Oscillation analysis for cryogenics valve
High computational cost
Time window length
Number of channels
Filters degree
Running system:
~3000 channels under analysis daily
Algorithm outcome validated by system experts (in terms of false positives)
5th Nov 2015
Siemens CERN openlab

6. PID supervision
In collaboration with the University of Valladolid (Ph.D. R. Martinez) Based on: “Performance monitoring of industrial controllers based on the predictability of controller behaviour”, R. Ghraizi, E. Martinez, C. de Prada
CERN control systems contains hundreds of thousands of control loops in operation
PID performance has an impact on:
Process security
Quality of physics
Maintenance (stress of the equipment)
Issues:
Many sources of faults / malfunctions
External disturbances / factors
Bad tuning
Wrong controller type / structure
Slow degradation
System status dependency
Process
Controller u w y SP CV v MV
5th Nov 2015
Siemens CERN openlab

7. Evaluation of PID supervision
Analysis algorithm:
Based on performance Harris index and error prediction
No a priori knowledge about the system under analysis
Running system:
More than 3000 control loops under analysis daily
Algorithm outcome validated by system experts (in terms of false positives)
High computation cost:
Order of the regression model
Prediction level
Size of the process data history
Time window size
Bad
Good
5th Nov 2015
Siemens CERN openlab

8. Integration of data analytical solutions into Siemens’ Smart Data Technologies
New version of WatchCAT
Extended support for R and Octave
Still a prototype version
Plug and play architecture
R implementation of the oscillation detection algorithm
Octave implementation of the algorithm for evaluation of PID supervision
Evaluation of the new version:
Improved performances and memory allocation
Feedback to Siemens
Code name “WatchCAT”
Data Fusion of events & sensors
Complex Event Processing
Automated Learning of fault patterns
Logical Reasoning for Fault Detection & Isolation
Fault prediction based on recognizable patterns
5th Nov 2015
Siemens CERN openlab

9. Cloud-based analytical solutions & Siemens’ Smart Data Technologies
Code name “ELVis”
Cloud-based BIG Data Analytics for Time Series Sensor Data
Real-Time Stream Processing at customizable KHz-Rates
High Performance Online Visualization in Rich Web-based UI
Intelligence for Sensor Data Validation
Job-based Online Data Analysis
Jupyter & Dockers
Slave
Slave
Master
Slave
Slave
NFS
Scripts
Data
Sharing code and results
Distribute computational load
Interactive analysis
Multi-language development environment
5th Nov 2015
Siemens CERN openlab

10. Status & Next Steps CERN & Siemens collaboration :
New analytical algorithms designed, implemented and integrated into Siemens analytical framework
Signal oscillation detection
Evaluation of PID supervision
Anomaly detection by sensors data mining [on going]
Evaluation of Siemens diagnostic tools
Cloud computing
ELVis as a Storm based solution
Jupyter and Docker as a cloud development environment for code sharing
“Formalizing expert knowledge in order to analyse CERN control system”, ICALECPS 2015
Continue the integration of CERN specific extensions & data analysis algorithms / solutions
Extensive deployment of Siemens cloud-based solutions for Big Data analytics as a Service
5th Nov 2015
Siemens CERN openlab

11. Thank you for your attention!
Any Questions
Thank you for your attention!
5th Nov 2015
Siemens CERN openlab