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Politecnico di Milano e Veneranda Fabbrica

del Duomo di Milano

Aula Magna – RettoratoMercoledì 27 maggio 2015

A Feature Selection-Based Approach for the Identification of Critical

Components in Complex Technical Infrastructures: Application to

the CERN Large Hadron Collider

Prof. Piero BARALDI Politecnico di Milano

Ing. Andrea CASTELLANO Politecnico di Milano

Dott. Ahmed SHOKRY Politecnico di Milano

Dott. Ugo GENTILE CERN, EN-ARP

Dott. Luigi SERIO CERN, EN-ARP

Prof. Enrico ZIO Politecnico di Milano

Luca Pinciroli

Complex Technical Infrastructures (CTIs)• Large-scale systems• 10.000+ components• Hierarchical architectures

What is the problem? 2

Andrea Castellano

Luca Pinciroli

Complex Technical Infrastructures (CTIs)• Large-scale systems• 10.000+ components• Hierarchical architectures

What is the relevance of the problem? 3

Andrea Castellano

Identification of the critical components

Allocation of preventive, mitigative, recovery solutions

Failures → Unreliability, risk, service loss, economic loss, ...

Luca Pinciroli

4

Andrea Castellano

Possible solution methods

Importance Measures• Birnbaum• Risk Achievement Worth• Fussel-Vesely• ... C

om

po

nen

t4

Co

mp

on

ent

67

Co

mp

on

ent

23

Co

mp

on

ent

39

Co

mp

on

ent

99

1° 2° 3° 4° 5° 6° 7° 8° 9° 10°

Ranking

Imp

ort

ance

Mea

sure Critical Components

(Dys)functional Logic (e.g. Fault Tree)

XCTI = Φ X1, X2, … , X8 ,

Xi = ቊ1 failed0 operating

, 𝑖 = 1,… , 8,

XCTI = ቊ1 failed0 operating

.

CTI Failure

Luca Pinciroli

5

Andrea Castellano

What are the scientific/technical issues? (1)

CTI• System complexity (number

of components, system’s structure, ...)

• Hidden dependencies• Time-dependence

(components upgrading, replacement, renovation, ...)

(Dys)functional logic partially unknown

Critical components

XCTI = Φ X1, X2, … , X8 ,

Xi = ቊ1 failed0 operating

, 𝑖 = 1,… , 8,

XCTI = ቊ1 failed0 operating

.

Luca Pinciroli

6

Andrea Castellano

What are the scientific/technical issues? (2)

Industry 4.0Operational Data

t [seconds]

y1 (t)

Digitalization

• N>>1 signals (e.g. 104)• L>>1 events per year (e.g. 104)

Luca Pinciroli

Innovative solution 7

Andrea Castellano

C1

C3

C5

FS1 =

𝑦1 𝑦2

𝑦7 𝑦8 𝑦9

𝑦13 𝑦14𝑦15

C2

C4

C5

FS2 =

𝑦3 𝑦4 𝑦5 𝑦6

𝑦10 𝑦11 𝑦12

𝑦13 𝑦14 𝑦15

Classifier

𝑥𝐶𝑇𝐼 = ቊ1 𝑓𝑎𝑖𝑙𝑒𝑑0 𝑠𝑎𝑓𝑒

A1

Classifier A2

CS1 is more critical than CS2 ⟺ A1 > A2

Components subset Features subset Classifier Classification accuracy

CS1

CS2

𝑥𝐶𝑇𝐼 = ቊ1 𝑓𝑎𝑖𝑙𝑒𝑑0 𝑠𝑎𝑓𝑒

Luca Pinciroli

Problem formulation 8

Andrea Castellano

ClassifierFeature

Selection

𝒚 =

𝑦1𝑦2𝑦3𝑦4…𝑦𝑁

All features

𝒚∗ =

𝑦4𝑦31𝑦54…

𝑦𝑁−3

Optimal classification

accuracy

𝑥𝐶𝑇𝐼 = ቊ1 𝑓𝑎𝑖𝑙𝑒𝑑0 𝑠𝑎𝑓𝑒

Selectedfeatures (signals)

Identification of the critical components

Feature selection problem

=

Luca Pinciroli

Possible solutions 9

Andrea Castellano

All signals

𝒚 =

𝑦1𝑦2𝑦3𝑦4…𝑦𝑁

𝒚∗ =

𝑦4𝑦31𝑦54…

𝑦𝑁−3

Optimal feature subset

Search engine

Candidate feature subset Classifier

Evaluation function = Accuracy

• Filter feature selection

Feature Selection• Wrapper feature selection

All signals

𝒚 =

𝑦1𝑦2𝑦3𝑦4…𝑦𝑁

𝒚∗ =

𝑦4𝑦31𝑦54…

𝑦𝑁−3

Optimal feature subset

Search engine

Candidate feature subset

Evaluation function = Clustering

performance

Feature Selection

Luca Pinciroli

Possible solutions 10

Andrea Castellano

All signals

𝒚 =

𝑦1𝑦2𝑦3𝑦4…𝑦𝑁

𝒚∗ =

𝑦4𝑦31𝑦54…

𝑦𝑁−3

Optimal feature subset

Search engine

Candidate feature subset Classifier

Evaluation function = Accuracy

• Filter feature selection

Feature Selection• Wrapper feature selection

All signals

𝒚 =

𝑦1𝑦2𝑦3𝑦4…𝑦𝑁

𝒚∗ =

𝑦4𝑦31𝑦54…

𝑦𝑁−3

Optimal feature subset

Search engine

Candidate feature subset

Evaluation function = Clustering

performance

Feature Selection

Luca Pinciroli

11

DataSupport Vectors

Cost-Sensitive Support Vector Machine (CS-SVM)

𝑦1

𝑦2

𝑦1, 𝑦2, 𝑥𝐶𝑇𝐼 = 0 1𝑂𝐿𝐷

𝑦1, 𝑦2, 𝑥𝐶𝑇𝐼 = 1 2OLD

𝑦1, 𝑦2, 𝑥𝐶𝑇𝐼 = 0 3𝑂𝐿𝐷

𝑦1, 𝑦2, 𝑥𝐶𝑇𝐼 = 0 4𝑂𝐿𝐷

...

𝑦1, 𝑦2, 𝑥𝐶𝑇𝐼 = 0 12𝑂𝐿𝐷

𝑦1

𝑦2

Decision boundary

100.1

Total error = 10.2

0.1 𝑥𝐶𝑇𝐼 = 1

𝑥𝐶𝑇𝐼 = 0

Investigated approach: Classifier

𝑦1

𝑦2

Andrea Castellano

Luca Pinciroli

Investigated approach: Search engine 12

Andrea Castellano

yNyN-1

Mutation Recombination Selection

Classification accuracy

❖ 𝑭𝒎𝒆𝒂𝒔𝒖𝒓𝒆 =𝟐

𝑻𝑷+𝑻𝑵

𝑻𝑷+𝑻𝑷+𝑭𝑷

𝑻𝑷

❖ 𝑮𝒎𝒆𝒂𝒏 = (𝑻𝑷

𝑻𝑷+𝑭𝑵+

𝑻𝑵

𝑻𝑵+𝑭𝑷)

Binary Differential Evolution (BDE)

• Candidate feature subset = chromosome

• Optimal subset search

Population of N chromosomes

𝑦1

𝑦2

𝑥𝐶𝑇𝐼 = 1

𝑥𝐶𝑇𝐼 = 0

𝑻𝑷𝑻𝑵

𝑭𝑷𝑭𝑵

y1 y2 y3 y4 y5

Luca Pinciroli

Case study 13

Andrea Castellano

Project developed during a 6 months internship at CERN, Geneva, Switzerland.The data and images related to CERN which are shown in this work are confidential and property of CERN (Copyright © CERN).

5.000+ components (transformers,

distribution switchboards,...) in 8 Sectors

10.000+ signals (power, current,...)

CERN LHC electrical network

Possible preventive dumping of LHC beams

𝑥𝐶𝑇𝐼 = ቊ1 𝑑𝑢𝑚𝑝𝑒𝑑0 𝑛𝑜𝑡 𝑑𝑢𝑚𝑝𝑒𝑑

Sector 5

Sector 6

Sector 7

Sector 8

Sector 1

Sector 4

Sector 2

Sector 3

Electrical disturbances

❖ Objective: Identification of the critical components

Luca Pinciroli

Case study: available information 14

Andrea Castellano

𝑥𝐶𝑇𝐼 Line Signal (Feature) Value Component Sector

0

66kV

1 y1 C1 1E

... ... ... ...

144 y144 C120 2E

18 kV

145 y145 C121 1E

... ... ... ...

5822 y5822 C5500 8E

t [s]

y1 (t)

tevent1

Year 2016: 3723 electrical disturbances3675 without dump (𝑥𝐶𝑇𝐼 = 0) 48 with dump (𝑥𝐶𝑇𝐼 = 1)

For each electrical disturbance:

Luca Pinciroli

15

Andrea Castellano

Results

EMD101_SLASH_5E_EA_DASH.EMD101_SLASH_7E_EA+EMD101_SLASH_7E_PM10_STAR

…EMD302_SLASH_1E_PM10_STAR

22 selected features

1E_2011E_2031E_208

…8E_205

19 critical components

Sector 4Sector 5

Sector 6

Sector 1

Sector 5

Luca Pinciroli

Results validation 16

Andrea Castellano

Developed approach Filter approach (RELIEF)

Computational time 1 week * 20 minutes *

Performance evaluation

False alarm rate = 7.3%Missed alarm rate = 25.0%

False alarm rate = 3.7%Missed alarm rate = 50.0%

Critical components agreement

• common• related

(functionally dependent)• disjoint

* 20 processors, 2.4 GHz clock rate, 130 GB memory

RELIEF

Developed approach

• Comparison with a filter feature selection approach

Luca Pinciroli

Conclusions 17

Andrea Castellano

Identification of critical components

CTI

Complex system

Monitored data Functional logic?

This thesis:

Classical Importance MeasuresAll signals

𝒚 =

𝑦1𝑦2𝑦3𝑦4…𝑦𝑁

𝒚∗ =

𝑦4𝑦31𝑦54…

𝑦𝑁−3

Optimal feature subset

Search engine

Candidate feature subset

Classifier

Performance evaluation

Case study:CERN LHC Electrical network

Sector 4Sector 5

Sector 6

Sector 1

Sector 5

Luca Pinciroli

Questions & Answers 18

Thank you

for your kind

attention

Andrea Castellano