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Artificial Intelligence – A Driving Force in Industrial 4.0Shaibal Barua, PhDResearcher, Artificial Intelligence and Intelligent Systems

shaibal.barua@mdh.se

29 May, 2020

● Artificial Intelligence – what’s the deal?

● Industrial Artificial Intelligence

● The applied AI workflow● Data cleaning and preparation● Data representation● AI problems and methods● Validation

● Use cases

● What’s next?

2

Outline

Part 1: Artificial Intelligence

3

Poll 1

Go to: www.menti.comUse the code: 16 54 26

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“The ability to learn, understand and think in a logical way about things; the ability to do this well”

- Oxford dictionary

5

Intelligence

Capability to understand complex ideas, ability to reasoning, learning from experiences, adaptability to the environment, plan, problem solving …..

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Poll 2

Go to: www.menti.comUse the code: 49 25 59

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Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans.

AI is the study of programmed systems that can simulate, to some extent, human activities

such as perceiving, thinking, learning and acting.

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Artificial Intelligence

Fig: Turing test

Behavior by a machine that, if performed by a human being, would be called intelligent

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Artificial Intelligence

Artificial Intelligence

Reasoning

Knowledge representationLearning

Planning Perception Robotics

Social Intelligence

Natural language

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Three types of AI

Narrow AI General AI Superintelligence

• Singular task• Successfully realized

to date• Operate under a

narrow set of constraints and limitations

• Machine intelligence• Carry out any

cognitive function that a human can

• Knowledge transfer between domains

• Fujitsu’s ”K” supercomputer

• Hypothetical agent• Machines become

self-aware • Surpass the capacity

of human intelligence

Poll 3

Go to: www.menti.comUse the code: 60 72 51

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● Sixth-fifth century BC● Aristotle layout the epistemological basis; introduces syllogistic logic● The Iliad – assorted automata from the workshops of Greek god

Hephaestus

● Late first century● Fable automata built by Heron of Alexander

● Fifteenth-sixteenth century● Mechanic clocks, Paracelus introduces a recipe for a humanculus, an

intelligent “little man”

● Eighteen century ● Philosophers try to formulate the laws of thought

● Nineteenth century● Literary artificial intelligences proliferation

● Twentieth century ● Alan Turin proposes an abstract of universal computing machine

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How Old is the idea of AI?

Hoffman’s The SandmanGoethe’s FaustMary Shelley’s Frankenstein

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AI: Past, Present and Future

Golden years: 1957-1974Symbolic AI, search algorithms, neural nets, industrial robots, etc.

Expert systems boom: 1980-1987Rule-based, logical systemsSelection of components based on customer requirements5th gen project (Japan)Neural networks, backprop.

Goals fulfilled: 1993-2011

Deep Blue (1997)Victory of the “neats” (2003)DARPA Grand Challenge (2005)AI untold successes in data mining, robotics, logistics, speech recognition, search engines

Deep learning, big data and general AI: 2011-presentAccess to large amounts of dataFaster computersDeep learning drives progress in image and video processing, text analysis, speech recognitionGoogle DeepMind defeats world champion in Go (2016)Widespread discussions around Strong AI:superhuman intelligence

The Turing test

"I propose to consider the question, 'Can machines think?’” (A. Turing, 1950)

An interrogator asks questions to an (unseen) person A. If A is replaced by an AI, can the interrogator detect this or not?

1 st AI Winter:

1974-1980

2 nd AI Winter:

1987-1993

2017 AlphaGo: Google’s AI beats world champion Ke

Jie. Notable for vast number of 2170 of possible positions

Poll 4

Go to: www.menti.comUse the code: 88 11 55

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● Ethical Reasoning● Accountability, Responsibility,

Transparency

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Responsible AI

Figure: Trolley problem dilemma

● Responsible AI concerned with the fact that decisions and actions taken by intelligent autonomous systems have consequences that can be seen as being of an ethical nature.

Figure: Interrelationship of the seven requirements: all are of equal importance, support each other, and should be implemented and evaluated throughout the AI system’s lifecycle

Source: EU Ethics Guidelines for Trustworthy AI, https://ec.europa.eu/futurium/en/ai-alliance-consultation/guidelines/1

Poll 5

Go to: www.menti.comUse the code: 71 62 93

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Part 2: Industrial ArtificialIntelligence

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1st Industrial Revolution1765

•Mechanical production•Industry instead of agriculture

as basis of economy•Water power•Steam engine

2nd Industrial Revolution1870

•Electricity, gas and oil•Combustion engine, steel industry, chemical

industry•Telegraph, telephone•Division of Labour (Taylorism), Mass

production (Ford)

3rd Industrial Revolution1969

•Nuclear energy•Electronics,

telecommunication, computers•Automation - PLCs, control

theory, PID regulators, etc.•Industrial robots

4th Industrial Revolution2011

The four industrial revolutions

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•Connected machines•Complex human-machine interaction•Artificial intelligence

A systematic discipline, which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance.

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Industrial Artificial Intelligence

Jay Lee, Hossein Davari, Jaskaran Singh, Vibhor Pandhare, Industrial Artificial Intelligence for industry 4.0-based manufacturingsystems, Manufacturing Letters, Volume 18, 2018, Pages 20-23,

AI and Industry 4.0

19Adapted from: Jinjiang Wang, Yulin Ma, Laibin Zhang, Robert X. Gao, Dazhong Wu, Deep learning for smart manufacturing: Methods and applications, Journal of Manufacturing Systems, Volume 48, Part C, 2018, Pages 144-156,

AI/ML Enabled Advanced Analytics

Capture Products’ Condition,

environment and operation

Descriptive(What happened)

Examine the causes of reduced

product performance or

detect failure

Diagnostics(Why it happened)

Predict quality and patterns that signal impending events

Predictive(What will happen)

Identify measures to improve outcomes or

correct problems

Prescriptive(What action to take)

Product Company Manufacturer Supplier

Data AggregationSmart, connected products(Location, condition, use, etc.)

Enterprise(Service histories, warranty

status, etc.)

External(Price, weather, supplier

inventory, etc.)

Deep insights

Data Processing

Decision making and applications

Smart Connected Process

Knowledge

Pattern

Data

● Analytics technology (A),

● Big data technology (B),

● Cloud or Cyber technology (C),

● Domain knowhow (D) and

● Evidence (E)

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Key elements in Industrial AI

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Industrial AI

Figure: Comparison of Industrial AI with other learning systems

Jay Lee, Hossein Davari, Jaskaran Singh, Vibhor Pandhare, Industrial Artificial Intelligence for industry 4.0-based manufacturingsystems, Manufacturing Letters, Volume 18, 2018, Pages 20-23,

● Machine-to-machine interactions

● Machine-to-human interactions

● Data quality

● Cyber security

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Challenges of Industrial AI

Poll 6

Go to: www.menti.comUse the code: 92 63 4

23

Part 3: The applied AI workflow

24

25

The Industrial AI stack

Deployment, maintenance and support

Validation

“Solving the problem”

Representation

Data collection and processing

Business UnderstandingBusiness Understanding

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Planning, scheduling, etc.

Common in industrial problems everywhere:● How should we schedule a workforce?● How to order manufacturing steps in a product variant?● How to order individual manufacturing orders/items?● On what units should which maintenance be performed and

when?… etc.

Typically, a deep understanding of the business is needed.

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The Industrial AI stack

Deployment, maintenance and support

Validation

“Solving the problem”

Representation

Data collection and processing

Business Understanding

Data from real applications is dirty:● Duplicates and missing data● Values with special meaning (ID 9999 means ”missing”)● Invalid data● Logically inconsistent data● Mystery data (railway cars which are 600 meters long)● Spiking data (temperature is 10e+10 for 1 millisecond)● Sensor drift, ”almost” values (0.6% really means 0.0%;

100.6% means 100%)● Multiple data files which are not in sync ● Misspellings● Different wordings

Data preparation and cleaning takes a long time!Validity threat: data cleaning removes realistic details

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Data cleaning and preparation

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Example

Poll 7

30

Go to: www.menti.comUse the code: 70 24 27

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The Industrial AI stack

Deployment, maintenance and support

Validation

“Solving the problem”

Representation

Data collection and processing

Business Understanding

● Before a method is chosen, the representation should be considered● For machine learning – what should be the input?● E.g. vibration/noise analysis – representation in time/space or frequency

domain?● For planning, scheduling, simulation – what model

abstraction should be used?● E.g. microscopic model of robot movements, mesoscopic model of discrete

manufacturing steps, or macroscopic model of completion time distribution for product variants.

● In both cases, the representation of the problem can impact performance substantially.

● Finding the right representation requires in-depth understanding of the application!

● Stakeholders must agree to modeling assumptions!

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Representation

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The Industrial AI stack

Deployment, maintenance and support

Validation

“Solving the problem”

Representation

Data collection and processing

Business Understanding

● ML tasks are typically classified into following categories, depending on the nature of the learning "signal" or "feedback" available:

● Supervised learning – it uses inputs and their desired outputs• The program is “trained” on a pre-defined set of “training

examples”, which then facilitate its ability to reach an accurate conclusion when given new data.

● Unsupervised learning - no labels are given to the learning algorithm• The program is given a bunch of data and must find

patterns and relationships therein.

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Types of ML

Some problems in SupervisedMachine Learning

35

Supervised Machine Learning

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Problems in supervised ML

In classification, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more of these classes.

In regression, the outputs are continuous rather than discrete.

- An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight.

37

38

Machine Learning Algorithms

ML

Logic

Graphical

modelSupport vectors

Neu

ral

Net

wor

ks

Genetic

Programs

Accuracy

Squa

red

Erro

r

Fitness

Posterior

Probability

Margin

Inverse Deduction

Gra

dien

t Des

cent

Probabilistic Inference Genetic Search

Constrained Optim

ization

Conn

ectio

nist

Evolutionaries

BayesiansAnalogizers

Symbolists

REPR. EVAL.OPT.

• REPR: Representation• EVAL: Evaluation• OPT: Optimization

Some problems in UnsupervisedMachine Learning

39

Unsupervised Machine learning

40

Problems in unsupervised ML

In clustering, a set of inputs is to be divided into groups. - Unlike in classification, the groups are not known

beforehand, making this typically an unsupervised task.

Density estimation finds the distribution of inputs in some space.

Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space.

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42

The Industrial AI stack

Deployment, maintenance and support

Validation

“Solving the problem”

Representation

Data collection and cleaning

Business Understanding

● During model building, random patterns in the sample areeasily found which are might not be present in the wholepopulation.

● To justify the performance of the built predictive model, thevalidation should be done with data points that were neverused while building the model.

● For a set of ML variants, optimize parameter selection (learn) on the training set.

● Find the ML variant which performs best on the cross-validation set (e.g. polynomial degree)

● Estimate the generalization error using the test set

43

Model Validation

● You’ve trained your model, now what?

● Overtraining – model doesn’t generalize to (perform well on) new data.

● “Validation” or evaluation is used to estimate the performance on new data, i.e. how the model would perform when actually used

● Validation results will always be too optimistic!

Overtraining

✕ Few data samples ✕ Complicated model ✕ Similar training, test and validation sets✕ Fine-tuning parameters ✕ Evaluating several models with the same validation set

Image by Chabacano / CC BY

● Training Set:● This set is used for training the predictive models.

● Validation Set:● Fixing the values of different parameters of the built model is donewith this set.

● Test Set:● Accuracy of the built model is determined using this set.

● In common practice, test set is made with larger part of thedata containing data points of all possible outcomes. The rest ofthe dataset is split into two sets for validation and testing.

● A widely used ratio of data splitting is 60:20:20 for training,validation and testing.

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Data Splitting

46

K-fold Cross Validation

CV # 1

CV # 2

CV # 3

CV # 4

CV # 5

Training Set Validation Set

Original Data (n = 20)

Example: Dataset Splitting in 5 – fold Cross Validation.

Confusion Matrix

47

Performance Measures

Case 1

Accuracy = 85%

● Class 1 = 10● Class 2 =10

9 1

2 8

Case 2

Accuracy = 90%

● Class 1 = 9● Class 2 =1

9 0

1 0

● F1 score tells us how precise and robust a model is.

● It is the harmonic mean of Precision andRecall values

!1 = 2 11

%&'()*)+, +1

.'(/00

● When the False Negatives and False Positives are crucial

● When there are imbalanced classes

● Greater F1 Score indicates better performance for prediction models.

48

F1 ScoreTP FP

FN TN

49

Performance Measures

● Receiver Operating Characteristics(ROC)● ROC is the widely used metric for

validating binary classification models.

● Two basic terms for AUC:● Sensitivity: In other words, it is called True

Positive Rate (TPR). Sensitivity is calculated fromthe values of confusion matrix –

!"#$%&%'%&( )*+ = )*)* + ./

● Specificity: It is also termed as False PositiveRate (FPR). It is calculated with the formula –

!0"1%2%1%&( .*+ = )/)/ + .*

● Example: Consider a test of Covid-19● Test has 90% sensitivity that means the test will

correctly return a positive result for 90% of peoplewho have the disease. But will return a negative result— a false-negative — for 10% of the people who havethe disease and should have tested positive.

● What about specificity?

Poll 8

Go to: www.menti.comUse the code: 23 58 72

50

51

The Industrial AI stack

Deployment, maintenance and support

Validation

“Solving the problem”

Representation

Data collection and cleaning

Business Understanding

Out of scope of this lecture

52

The Industrial AI stack in reality

Deployment, maintenance and support

Validation

“Solving the problem”

Representation

Data collection and cleaning

Business Understanding

Often 80% of total effort

Foundations of value creation (20% of effort)

End-user value

Part 4: Use cases

53

Example: Machine Health Monitoring

54

ConventionalData-driven

MHMS

Physical-basedMHMS

Deep Learning based MHMS

Solution

Monitored Machine

Data Acquisition

Solution

Solution

Monitored Machine

Monitored Machine

Data Acquisition

Data Acquisition

Hand design physical model

Source: Rui Zhao, Ruqiang Yan, Zhenghua Chen, Kezhi Mao, Peng Wang, Robert X. Gao, Deep learning and its applications to machine health monitoring, Mechanical Systems and Signal Processing, Volume 115, 2019, Pages 213-237, ISSN 0888-3270

Example: Data Analytics in Industry 4.0

55

A Case Study in Power Transfer Unit

Project: AUTOMADProject Leader: Dr. Mobyen Uddin Ahmed, Docent Contact: mobyen.ahmed@mdh.se

Example: Monitoring and Quality Control

56

Prototype running at Volvo and Chalmers, cloud based solution,implemented by Ivan Tomašić MDH

Project Leader: Prof. Peter Funk, MDH Contact: peter.funk@mdh.se

Example: Pulp and paper

58Pr

oduc

tion

Rat

e

Qua

lityOpe

rati

ng c

ostDetection of Digester Faults

• Screen clogging.

• Hang ups and

• ChannellingDetection of Anomalies

• Sensor faults

• Something is wrong

Prediction

• Kappa value

Contact: shaibal.barua@mdh.se and tomas.olsson@ri.se

Poll 9

59

Go to: www.menti.comUse the code: 40 40 42

● Big Data and Cloud Computing for Industrial Applications ● Study period 2020-11-09 - 2021-01-17

● Visit mdh.se/premium

60

Production engineering course autumn 2020

61

Interesting Reading

Thank you

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