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Firma convenzione
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
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