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RISE Research Institutes of Sweden
AI I PROCESS-
INDUSTRINFör ett hållbart och konkurrenskraftigt Sverige
Stig Larsson
ICT
SICS Västerås
E-DIG Digital lärplattform
Digitalt kvalitetscertifikat vid 3d-printing
Maskininlärning genom automatisk analys av stora datamängder
Flexibel robotisering för små- och medelstora produktionsföretag
Det smarta digitala sågverket
Kommunikation och underhåll i den digitala fabriken
3d-printing av verktyg
DIGFOG Digitaliserade fogningsprocesser
2
Vinnovas satsning ”Den smarta digitala fabriken”
▪ Finns över hela Sverige – och lite till.
▪ 2 300 medarbetare, varav 30 % disputerade forskare. +355 nya medarbetare från Swereafrån den 1 oktober.
▪ Omsatte 2017 cirka 2,7 miljarder SEK.
▪ En stor del av kunderna är små- och medelstora företag som står för ca 30 procent av omsättningen.
▪ Driver ett 100-tal test- och demonstrations-miljöer, öppna för företag och lärosäten (Ägare och partner i 60 % av Sveriges samlade test- och demonstrationsmiljöer).
3
Fakta om RISE-koncernen
Med vår kompetensbredd och unika
expertis skapar vi nytta för mångaBioekonomi Brand & risk
Cement & betong Certifiering Cirkulär ekonomi Design Elektronik Energi & bränslen
Förpackning Glas Hälsa, vård & omsorg ICT och Telecom Jordbruk & livsmedel Kemi, material & ytor
Life Science Maritim Maskinteknik Mekanik Metrologi & mätteknik Papper och massa
Processutveckling Samhällsbyggnad Säkerhet Transport Trä Vatten
5
▪ Varför intressant just nu?
▪ Processorkraft
▪ Minne
▪ Algoritmer
Artificiell intelligens, AI
Artificiell intelligens
Maskininlärning
Deep learning
1956 1980´s 2000´s
Text från olika källor
Domain-agnostic discovery of similarities and concepts at scale, Olof Gornerup, Daniel Gillblad,
Theodore Vasiloudis, Knowledge and Information Systems, Sep 2016
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product
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communications
software
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productivity
mortgages
revenues
yieldsairlines
incomes
prices
power
orders
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volumes
earnings
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output
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shipments
hardware
management
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clothing
ratio
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values permitspremiums
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sales
activity
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yield
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Fig. 10 Word similarity graph. In the electronic version of the paper, concepts are color-coded and vertexlabels are visible after zooming. The visualization is done with Cytoscape [43]
The correlation graph consists of vertices with pi ≥ 10− 5 and edges with ρi , j ≥ 4 bits,
and has a maximum in-degree of 1000 edges. When clustering the corresponding similarity
graph, σm = 0.25, queues have capacity 4, and the iteration described in Sect. 4.3.2 is
performed 16 times. The resulting cluster assignments are then given by labels that occupy
at least 50% per queue. Out of these, the most dominant cluster assignments per vertex are
depicted in Fig. 10.
As examples, a random set of clusters is shown in Table 1, and a set of clusters of our
choosing in Table2. Both sets—with someexceptions(e.g., { britain, nation} , perhapsdueto
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O. Görnerup et al.
0 200 400 600 800 1000
In-degree
0.0
0.2
0.4
0.6
0.8
1.0
Dis
trib
uti
on
fun
ctio
n
Fig. 6 The cumulative distribution function of in-degrees for the artist correlation graph
Richard Wagner
Sir Edward Elgar
Giuseppe Verdi
Franz Liszt
Gustav Mahler
Felix Mendelssohn
Jean Sibelius
Franz SchubertRobert Schumann
Georg Friedrich Händel
Johannes Brahms
Joseph Haydn
Gabriel Fauré
Maurice Ravel
Claude Debussy
Tomaso Giovanni Albinoni
Antonio VivaldiAntonín Dvo
Rakim
RaekwonSunz Of Man
Wu-Tang Clan
Masta Killa
Afu-RaU-God
CappadonnaGang Starr
Black Star
Lloyd Banks
Gza/Genius
Nas Inspectah Deck
Method Man & Redman Az
Killah Priest Jeru The Damaja
Method Man
Ol' Dirty Bastard
D.I.T.C.
Ghostface Killah
Classic Wu-Tang Instrumentals
Masta Ace
Mobb Deep
Army Of The Pharaohs
Rza
Shyheim
Big L
Black Uhuru
The Congos
Culture
Gregory Isaacs
Jacob Miller
Beenie Man
The Mighty DiamondsDry & Heavy
Israel Vibration
Peter Tosh
Horace Andy
The Upsetters
Cornell Campbell
Buju Banton
Augustus Pablo
Barrington Levy
CapletonMax Romeo
The Abyssinians
Anthony B
Sizzla
Burning Spear
Bunny Wailer
Fig. 7 Examplesof components in an artist similarity graph correspond to threedistinct music genres. Edgeswith weightsσi , j ≥ 0.5 are shown
to represent musical genres as exemplified in Fig. 7. As such, the similarity graph could be
used in amusic recommendation system to relate similar artists through the listening habits
of users, similar to a collaborative filtering system. We could then also provide an intuitive
way to incorporate thepopularity of artistsvia their play frequencies in order to mitigate the
effect of popularity bias in recommendations [9].
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Teckenspråkstolkning – hand och kropp
7
• Vad behövs för att bygga automatisk översättning från teckenspråk till tal/text?
• En utmaning: svåra bildförhållanden
Ett samarbete mellan RISE (Interactive & SICS) och Devmito AB
Påverkar alla områden och branscher
AI idag:
fokus på
datadrivna
tillämpningar Data-analys
Data Science
Artificiellintelligens
Maskin-inlärning
Hjärtat som driver det datadrivna: maskininlärning
Vad lär man sig från?
• Alla slags data
• Tidsserier
• Naturligt språk / text
• Bilder, video, ljud
Vad lära sig?
• Klassificering
• Klustring
• Prediktion
• Avvikelsedetektion
Vad används det till?
• Rekommendationssystem
• Upptäcka bedrägerier
• Medicinsk diagnos
• Upptäcka fel i utrustning
• Ansiktsigenkänning
• Taligenkänning
• Översättning
• …
Förmågan att lära
sig och kunna göra
förutsägelser från
exempel
11
BIG DATA
= BIG VALUE ?
12
RÄTT DATA
+ en hel del arbete
= BIG VALUE
Samla in data
Förbereda data
Träning av
modell
Resultat analys
Prediktion
13
Maskininlärning
▪Processoptimering/prognos▪ Vilka parameterinställningar ger bäst kvalitét och
minst produktionsavbrott?
▪Upptäcka det onormala▪ Är detta ett normalt beteende eller avviker det
mycket från det förväntade?
▪Upptäcka felaktigheter▪ Kommer denna process leda till
produktionsavbrott eller ett känt fel på maskinen?
14
Exempel på tillämpningar
Maskininlärning
Exempel
Underhåll och optimering av maskiner och processer
1510/11/15
Ökad tillgänglighet
Nya typer av ”produkter”
Noll oplanerade
stopp
Ökad kvalitétFörlängd livslängd
CC BY-SA 3.0 Bharat S Raj
CC0
Public domain
Underhåll och optimering av maskiner och processer
1610/11/15
Systemkomplexitet
Datamängd och snabbhet
Begränsade
resurser
Dålig integration
CC BY-SA 3.0 Bharat S Raj
CC0
Public domain
▪ Avhjälpande underhåll
▪ Förebyggande underhåll
▪ Schemalagt underhåll
▪ Tillståndsbaserat underhåll
▪ Prediktivt underhåll
17
Underhåll
I I I
X
I
I
tid
Fel: X Underhåll: I
I I I
I
XI
I
▪”Datadrivet underhåll sparar pengar för industrin”
▪”…spara 20-40 procent av kostnaderna för underhåll.”
18
Resultat från forsknings*
*http://www.forskning.se/2016/11/30/datadrivet-underhall-sparar-
pengar-for-industrin/
20-40%
Kostnad
19
Vilken blomma är det? https://en.wikipedia.org/wiki/Iris_flower_data_set#Data_set
Egenskaper Etikett
F(x) = y
20
▪ Arbestgång, itereras:
▪ Välj egenskaper
▪ Välj metod och modell
▪ Träna modellen
▪ Testa modellen
▪ Utmaningar:
▪ Välj rätt egenskaper
▪ Välj rätt metod för att bestämma F
Samband mellan egenskaper och etikett
▪ https://scikit-learn.org/stable/user_guide.html
21
Att välja metod
Klassificering
10/11/15 22
Regression
Regression
25
Data från processindustri
Avvikelsedetektion
10/11/15 26
27
Visualisering av alarm
RISE Research Institutes of Sweden
EXEMPEL FRÅN
STÅLINDUSTRIN
28
CASE 1
29
▪ Undersökning av orsak till haveri
30
Data
31
Underhåll, stopp eller haveri?
32
Varians
33
Identifierad anomali
Tid för haveri
Underhåll
Avvikande trend för vals 5, variansen för ett referensvärde
34
Orsak till större varians för vals 5
35
Avvikelse för medelvärden för skevhet mellan olika sidor för
vals 4
36
Data för nytt haveri
Behövs mer
data bakåt i
tiden
37
Nya data, varians
Inga slutsatser
baserat på
tillgängligt data
▪ I denna fallstudie:
• Metadata saknas
• PLC-data?
• Lagring av data
• Förstå data och processen
▪ Generellt:
• Vilka data är tillgänglig?
• Hur är data från olika system kopplade?
• Långsiktig användning
38
Utmaningar
CASE 2
39
▪ Effektivare underhåll värmeväxlare
40
▪ Effektivisera tvätt av filter i
värmeväxlare genom att prediktera
behov av tvätt
Underhåll VVX
41
Mätningar VVX1
42
Vvx1: difftryck_ÄV/[temp_p_in-temp_av_in]
43
10 dagar vvx1
44
Korrelation mätvärde under längre period
▪ Resultat
▪ Inga konkreta analysresultat
▪ Bra underlag för visualisering av analys
▪ Framgångsfaktorer för dataanalys
▪ Förståelse för process och problembild
▪ Gemensam analysgrupp med olika kompetenser och experter
▪ Öppet klimat
▪ Fungerande datainsamling
▪ Rätt urval av data kräver initial och kontinuerlig diskussion om process och problem
▪ System för att lätt komma åt data
▪ Rätt typ av data
▪ Data måste innehålla anomalier för att möjliggöra lärandet
▪ Stora mängder data under längre perioder
45
Sammanfattning dataanalys
RISE Research Institutes of Sweden
KONTAKTUPPGIFTER
Stig Larsson
072 563 7876
ICT
SICS Västerås