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

CERN openlab technical workshop

› 5th Nov 2015

Siemens collaboration

Filippo Tilaro,

EN/ICE CERN

2

Analytical framework for the CERN control system

5th Nov 2015 Siemens CERN openlab

Data Analysis Framework

Data collection & feedback

FFT

MachineLearning

Neural Network

CEP

Patterns

(R)

(LabView)

(Java)

(WatchCAT)

Data Processing Modules

Scalable and fault-tolerant !!!

Expert

Visualisation

Analysis memory and configuration

HistoricalData

Fieldbus

TN

PLCs

Sensors &

Actuators

MOON(Monitoring)

High Voltage

DIM/CMW OPC

Field layer

Processlayer

Supervision

layer

+50M channels

• + 2000 PLCs• + 650 FECs

+600 Application Services

35th Nov 2015 Siemens CERN openlab

› 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

Analytical areas of interest

Oscillation analysis for cryogenics valves

Anomaly detection by sensors data mining

PID supervision

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› 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 S7-416-2DP 30000 conceptual objects/parameters

› Valve oscillation can affect: Control system stability Maintenance (stress on the equipment) Performances & Safety Increase in the data load

LHC Cryogenics system and valve oscillation

5th Nov 2015 Siemens CERN openlab

Wrong valve oscillation

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› 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)

Oscillation analysis for cryogenics valve

5th Nov 2015 Siemens CERN openlab

6

› 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

PID supervision

5th Nov 2015 Siemens CERN openlab

ProcessControlleruw y

SP CV

v

MV

TT

uTC

w

q T

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› 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

Evaluation of PID supervision

5th Nov 2015 Siemens CERN openlab

Bad

Good

8

Integration of data analytical solutions into Siemens’ Smart Data Technologies

Siemens CERN openlab

› 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

› 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

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Cloud-based analytical solutions &Siemens’ Smart Data Technologies

Siemens CERN openlab

› 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

5th Nov 2015

SlaveSlave

Master

Slave Slave

NFSScripts

Data

Sharing code and results Distribute computational load Interactive analysis Multi-language development

environment

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› 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

CERN & Siemens collaboration :

Status & Next Steps

Siemens CERN openlab5th Nov 2015

Any Questions

Thank you for your attention!

Siemens CERN openlab 115th Nov 2015