Machine Learning and Deep Learning in Manufacturing

03/14/2017

Prof. Seungchul Lee

1

Introduction

• Since 2013 July: UNIST

– School of Mechanical Engineering

• 2010, Ph.D. from the University of Michigan, Ann Arbor

– S. M. Wu Manufacturing Research Center

– The Center of Intelligent Maintenance Systems (IMS)

• 2007, M.S. from the University of Michigan, Ann Arbor

• 2005, B.S. of Electrical Engineering from Seoul National University

• 2001, B.S. of Mechanical Engineering from Seoul National University

2

Robot Playing Piano

3By iSystems

How to Make Machine Intelligent ?

4

Artificial Intelligence

Machine Learning for Machine Intelligence

5

Classification

Regression

Clustering Dim reduction

Deep Learning for Machine Intelligence

6

CNN

RNN

Outline

• Machine Learning– Supervised Learning

– Unsupervised Learning

• Deep Learning: CNN

• Dimension Reduction– Latent Space

• Deep Learning: RNN

• (Model-augmented) Bayesian Machine Learning

7

Framework of Machine Learning

8

Sensor Data

Data Window

Features

Decision

Data acquisition and pre-processing

Windowing

Feature extraction

Model building and Classification (Inference)

Implementation of Machine Learning

9

Weather Station

Weather Station: Visualization

temperature

humidity

brightness

Data Science: (Unexpected) Hidden Information

Jul 31 10:02

Grad. Student came to lab.

Jul 30 23:26

Grad. Students

went home

Jul 31 05:27

Sunrise

Brightness Data

Framework of Machine Learning

13

Sensor Data

Data Window

Features

Decision

Data acquisition and pre-processing

Windowing

Feature extraction

Model building and Classification (Inference)

Outline

• Machine Learning– Supervised Learning

– Unsupervised Learning

• Deep Learning: CNN

• Dimension Reduction– Latent Space

• Deep Learning: RNN

• (Model-augmented) Bayesian Machine Learning

14

Supervised Learning

• Data with labels

• Classification

• Example– Rotating machinery (power plant)

– Data-driven diagnostics

– Training data set

15

chair desk

…

Web-based Monitoring Dashboard

16

Probability of Classification

normal

unbalance

misalignment

Outline

• Machine Learning– Supervised Learning

– Unsupervised Learning

• Deep Learning: CNN

• Dimension Reduction– Latent Space

• Deep Learning: RNN

• (Model-augmented) Bayesian Machine Learning

17

Unsupervised Learning

• No labels

• Representation

• Abstraction

• Clustering

18

Serov Motor

• System configuration– Arduino

– Servo motor

• Load (anomaly) generation– Anomaly is induced through manual press

19

Demo for Unsupervised Learning

20

Th

reshold

Outline

• Machine Learning– Supervised Learning

– Unsupervised Learning

• Deep Learning: CNN

• Dimension Reduction– Latent Space

• Deep Learning: RNN

• (Model-augmented) Bayesian Machine Learning

21

(Traditional) Machine Learning

22

Neural Network

23

Neural Network

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Deep Neural Network

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Convolutional Neural Networks (CNN)

• 이미지분류에서높은성능을보인 CNN 기법을진동신호기반결함진단에사용제안

26

Training Data Feature Extraction Classification

DiagnosticsDeep Learning

CNN on STFT Image

• Time series as input in PHM

• 기본 CNN구조를활용하기위하여신호를이미지화 (STFT spectrogram)

27

STFT image

signal

Outline

• Machine Learning– Supervised Learning

– Unsupervised Learning

• Deep Learning: CNN

• Dimension Reduction– Latent Space

• Deep Learning: RNN

• (Model-augmented) Bayesian Machine Learning

28

Dimension Reduction

• Principal Component Analysis (PCA)– Dim reduction without losing too much information

29

u1

u2

1

1

2

xu

x

Dimension Reduction

• PCA in time signals

30

31

Provide compressed representations

Deep Learning: Autoencoder

Outline

• Machine Learning– Supervised Learning

– Unsupervised Learning

• Deep Learning: CNN

• Dimension Reduction– Latent Space

• Deep Learning: RNN

• (Model-augmented) Bayesian Machine Learning

32

Input Latent Variable Classification

• Latent Variable = Hidden State

• Hidden state is not directly visible, but

• Decision (target) depends on hidden state

• Time sequential data Recurrent Neural Network

33

Hidden

State h1

h2 h3 h4 h5 h6

Deep Learning: RNN for Classification

• Deep learning structure for sequential data– Recurrent Neural Network (RNN)

– Information to be passed

34

diagnosis

W

…

…

…

State 3W

…

…

…

State 4

4o

Window 1

…

…

…

State 1W

Window 2

…

…

…

State 2Hidden

State

Input

Window 3 Window 4

Any Problems So for ?

35

Outline

• Machine Learning– Supervised Learning

– Unsupervised Learning

• Deep Learning: CNN

• Dimension Reduction– Latent Space

• Deep Learning: RNN

• (Model-augmented) Bayesian Machine Learning

36

Motivation: Robotics Application

37

Bayesian Estimation

• Dynamics (not considered yet)

• Intuition behind Bayesian Inference (Kalman filter)– Sequential measurements

– Noise

– True state?

– 예측가능

38

Bayesian Inference: Model as A Prior

• Machine learning: Generative

• Assumption– Latent variables are independent

• Pattern matching problem– Snapshot data

– No sequential (historical) consideration

• May not fully utilize all information (Variable dynamics)

39

X

Y

X

Y

X

Y

Latent Variable

Observation

Measurement

Tool Wear Estimation

• Hidden Markov Model

40

Demo for Model-based FDI

• Residual

• Kalman filter

41

Forecasting

• Prognostics

• The ultimate goal of PHM

• If models are good (?)– will provide more accurate RUL prediction

42

( ) ( ) ( )

( ) ( )

x t Ax t Bu t

y t Cx t

0.9 0.1

0 0.8

0 0

0 0 0

A

0 100 200 300 400 500 6001

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

C/1

Ca

pa

city (

Ah

r)

Cycles

B0005

Monte-Carlo Simulation based on PEMs model

0 100 200 300 400 500 6001

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

C/1

Ca

pa

city (

Ah

r)

Cycles

B0005

Monte-Carlo Simulation based on PEMs model

Data Science in Manufacturing

• 양품/불량검사– 현장전문가또는생산기술연구소엔지니어가경험과 domain

knowledge 를이용해측정신호로 rule 기반검사

• 설비상태– Long-term data

• Product data

• Manufacturing Service

43