Instandhaltung im Kinderzimmer – Predictive Maintenance am Modell erklärt (Seacon 2015)

© Zühlke 2015

Predictive Maintenance ExplainedMaintenance in the kids' room

Predictive Maintenance Explained | Nicolaj Kirchhof, Tim Speier May 2015 Slide 1

© Zühlke 2015

Agenda

• Building a CNC machine 101

• Maintenance of Wearing Parts

• Building a Prediction Model

• Applying the Model

• Using the right Tools


© Zühlke 2015

CNC Milling Machine


© Zühlke 2015May 2015Predictive Maintenance Explained | Nicolaj Kirchhof, Tim Speier

• Ranges: – X axis 17cm– Y axis 7cm– Z axis 4cm

• Spindle speed: 4500 rpm

• Feed rate: 22mm / min

• About 900 individual parts

• Roughly 20h construction effort

• Approximately 1200 LoC

Slide 4

CNC Milling Machine

The Facts

© Zühlke 2015

G1 Y4.004 Z0.2729

G1 Y3.979 Z0.2691

G1 Y3.954 Z0.2654

G1 Y3.829 Z0.2465

G1 Y3.804 Z0.2417

G1 Y3.679 Z0.2163

G1 Y3.629 Z0.2062

G1 Y3.604 Z0.2001

G1 Y4.554

G1 Y4.529

G1 Y4.454

G1 Y4.429 Z0.2973

G1 Y4.404

G1 Y4.379 Z0.2969

G1 Y4.354 Z0.2962

G1 Y4.329 Z0.2955

G1 Y4.229 Z0.2928

G1 Y4.204 Z0.2916

G1 Y1.179 Z-0.0025

G1 Y1.1581

G1 F30.0 X0.2601

Y1.0964

G1 F300.0 Y1.104

G1 Y1.129 Z0.0973

G1 Y4.104 Z0.2827

G1 Y4.029 Z0.276

G1 Y4.004 Z0.2729

G1 Y3.979 Z0.2691

G1 Y3.954 Z0.2654

G1 Y3.829 Z0.2465

G1 Y3.804 Z0.2417

G1 Y3.679 Z0.2163

G1 Y3.629 Z0.2062

G1 Y3.604 Z0.2001

G1 Y3.429 Z0.1543

G1 Y3.404 Z0.147

G1 Y3.329 Z0.123

G1 Y3.304 Z0.1152

G1 Y3.229 Z0.0916

G1 Y3.204 Z0.0833

G1 Y3.004 Z0.009

G1 Y2.979 Z-0.0025

G1 Y2.9564

G1 F30.0 X4.3101

Y3.0493

G1 F300.0 Y3.079

G0 Z1.0

G0 X0.1101 Y1.2205

G1 F30.0 Z-0.0025

G1 F300.0 Y1.4452

G1 F30.0 X0.1851 Y1.5275

G1 F300.0 Y1.504

G1 Y1.479 Z0.001

G1 Y1.404 Z0.0333

G1 Y1.204

G1 Y1.179 Z-0.0025

G1 Y1.1581

G1 F30.0 X0.2601 Y1.0964

G1 F300.0 Y1.104

G1 Y1.129 Z0.0973

G1 Y1.329

G1 Y1.379 Z0.0791

G1 Y1.404 Z0.0688

G1 Y1.554 Z0.0041

G1 Y1.6098

G1 F30.0 X0.3351 Y1.6921

G1 F300.0 Y1.654

G1 Y1.629 Z0.0072

G1 Y1.479 Z0.0719

G1 Y1.454 Z0.0817

G1 Y1.304 Z0.1363

G1 Y1.279 Z0.1451

G1 Y1.079

G1 Y1.054 Z-0.0025

G1 Y1.0352

G1 F30.0 X0.4101 Y0.9747

G1 F300.0 Y1.004

G1 Y1.029 Z0.1844

G1 Y1.229

G1 Y1.354 Z0.1472

G1 Y1.379 Z0.139

G1 Y1.529 Z0.0844

G1 Y1.554 Z0.0751

G1 Y1.704 Z0.0104

G1 Y1.729 Z-0.0004

G1 Y2.754 Z0.0543

G1 Y2.779 Z0.0435

G1 Y2.879 Z0.0004#

G1 Y2.779 Z0.0435

G1 Y2.904 Z-0.0025

G1 Y2.9262

G1 F30.0 X1.5351 Y3.0085

G1 F300.0 Y2.979

G1 Y2.954 Z0.0035

G1 Y2.854 Z0.0466

G1 Y1.754 Z0.2915

G1 Y1.779 Z0.2905

G1 Y1.879 Z0.2804

G1 Y1.954 Z0.2728

G1 Y1.979 Z0.2693

G1 Y2.054 Z0.2568

G1 Y2.129 Z0.2444

G1 Y2.154 Z0.2399

G1 Y2.229 Z0.2225

G1 Y2.254 Z0.2168

G1 Y2.329 Z0.1994

G1 Y2.354 Z0.1923

G1 Y2.404 Z0.1774

G1 Y2.429 Z0.17

G1 Y2.504 Z0.1477

G1 Y2.529 Z0.1396

G1 Y2.579 Z0.1214

G1 Y2.604 Z0.1123

G1 Y2.679 Z0.085

G1 Y2.704 Z0.0758

G1 Y2.754 Z0.0543

G1 Y2.779 Z0.0435

G1 Y2.879 Z0.0004

G1 Y2.904 Z-0.0025

G1 Y2.9262

G1 F30.0 X1.5351 Y3.0085

G1 F300.0 Y2.979

G1 Y2.954 Z0.0035

G1 Y2.854 Z0.0466

G1 Y2.829 Z0.0574

G1 Y3.954 Z0.2654

G1 Y3.829 Z0.2465

G1 Y3.679 Z0.2163

G1 Y3.629 Z0.2062

G1 Y3.604 Z0.2001

G1 Y3.429 Z0.1543

G1 Y3.404 Z0.147

G1 Y3.329 Z0.123

G1 Y3.304 Z0.1152

G1 Y3.229 Z0.0916

G1 Y3.204 Z0.0833

G1 Y3.004 Z0.009

G1 Y2.979 Z-0.0025

G1 Y2.9564

G1 F30.0 X4.3101 Y3.0493

G1 F300.0 Y3.079

G1 Y2.804 Z0.0682

G1 Y2.779 Z0.0785

G1 Y4.554

G1 Y4.529

G1 Y4.454

G1 Y4.429 Z0.2973

G1 Y4.404

G1 Y4.379 Z0.2969

G1 Y4.354 Z0.2962

G1 Y4.329 Z0.2955

G1 Y4.229 Z0.2928

G1 Y4.204 Z0.2916

G1 Y1.179 Z-0.0025

G1 Y1.1581

G1 F30.0 X0.2601 Y1.0964

G1 F300.0 Y1.104

G1 Y1.129 Z0.0973

G1 Y4.104 Z0.2827

G1 Y4.029 Z0.276

G1 Y4.004 Z0.2729

G1 Y3.979 Z0.2691

G1 Y3.954 Z0.2654

G1 Y3.829 Z0.2465

G1 Y3.804 Z0.2417

G1 Y3.679 Z0.2163

G1 Y3.629 Z0.2062

G1 Y3.604 Z0.2001

G1 Y3.429 Z0.1543

G1 Y3.404 Z0.147

G1 Y3.329 Z0.123

G1 Y3.304 Z0.1152

G1 Y3.229 Z0.0916

G1 Y3.204 Z0.0833

G1 Y3.004 Z0.009

G1 Y2.979 Z-0.0025

G1 Y2.9564

G1 F30.0 X4.3101 Y3.0493

G1 F300.0 Y3.079

G1 Y3.104 Z0.0115

G1 Y3.279 Z0.0765

G1 Y3.304 Z0.0859

G1 Y3.329 Z0.0939

G1 Y3.504 Z0.1498

G1 Y3.529 Z0.1566

G1 Y3.704 Z0.2024

G1 Y3.729 Z0.2079

G1 Y3.754 Z0.213

G1 Y1.179 Z-0.0025

G1 Y1.1581

G0 Z1.0

G0 X0.1101 Y1.2205

G1 F30.0 Z-0.0025

G1 F300.0 Y1.4452

G1 F30.0 X0.1851 Y1.5275

G1 F300.0 Y1.504

G1 Y1.479 Z0.001

G1 Y1.404 Z0.0333

G1 Y1.204

G1 Y1.179 Z-0.0025

G1 Y1.1581

G1 F30.0 X0.2601 Y1.0964

G1 F300.0 Y1.104

G1 Y1.129 Z0.0973

G1 Y1.329

G1 Y1.379 Z0.0791

G1 Y1.404 Z0.0688

G1 Y1.554 Z0.0041

G1 Y1.579 Z-0.0025

G1 Y1.6098

G1 F30.0 X0.3351 Y1.6921

G1 F300.0 Y1.654

G1 Y1.629 Z0.0072

G1 Y1.479 Z0.0719

G1 Y1.454 Z0.0817

G1 Y1.304 Z0.1363

G1 Y1.279 Z0.1451

G1 Y1.079

G1 Y1.054 Z-0.0025

G1 Y1.0352

G1 F30.0 X0.4101 Y0.9747

G1 F300.0 Y1.004

G1 Y1.029 Z0.1844

G1 Y1.229

G1 Y1.354 Z0.1472

G1 Y1.379 Z0.139

G1 Y3.404 Z0.147

G1 Y3.329 Z0.123

G1 Y3.304 Z0.1152

G1 Y3.229 Z0.0916

G1 Y3.204 Z0.0833

G1 Y3.004 Z0.009

G1 Y2.979 Z-0.0025

G1 Y2.9564

G1 F30.0 X4.3101 Y3.0493

G1 F300.0 Y3.079

G1 Y3.104 Z0.0115

G1 Y3.279 Z0.0765Slide 5

G1 Y1.754 Z0.2915

G1 Y1.779 Z0.2905

G1 Y1.879 Z0.2804

G1 Y1.954 Z0.2728

G1 Y1.979 Z0.2693

G1 Y2.054 Z0.2568

G1 Y2.129 Z0.2444

G1 Y2.154 Z0.2399

G1 Y2.229 Z0.2225

G1 Y2.254 Z0.2168

G1 Y2.329 Z0.1994

G1 Y2.354 Z0.1923

G1 Y2.404 Z0.1774

G1 Y2.429 Z0.17

G1 Y2.504 Z0.1477

G1 Y2.529 Z0.1396

G1 Y2.579 Z0.1214

G1 Y2.604 Z0.1123

G1 Y2.679 Z0.085

G1 Y2.704 Z0.0758

G1 Y2.754 Z0.0543

G1 Y2.779 Z0.0435

G1 Y2.879 Z0.0004

G1 Y2.904 Z-0.0025

G1 Y2.9262

G1 F30.0 X1.5351 Y3.0085

G1 F300.0 Y2.979

G1 Y2.954 Z0.0035

G1 Y2.854 Z0.0466

G1 Y2.829 Z0.0574

G1 Y3.954 Z0.2654

G1 Y3.829 Z0.2465

G1 Y3.679 Z0.2163

G1 Y3.629 Z0.2062

G1 Y3.604 Z0.2001

G1 Y3.429 Z0.1543

G1 Y3.404 Z0.147

G1 Y3.329 Z0.123

G1 Y3.304 Z0.1152

G1 Y3.229 Z0.0916

G1 Y3.204 Z0.0833

G1 Y3.004 Z0.009

G1 Y2.979 Z-0.0025

G1 Y2.9564

G1 F30.0 X4.3101 Y3.0493

G1 F300.0 Y3.079

G1 Y2.804 Z0.0682

G1 Y2.779 Z0.0785

© Zühlke 2015

CNC Milling Machine

Predictive Maintenance Explained | Nicolaj Kirchhof, Tim Speier

Tool Wear

May 2015 Slide 7

• No direct measuring of wear

• Wear depends on work piece

Picture: Elco HSC HPC Fraeser

• Increase tool life

• Increase service life

• Reduce unplanned downtime

• Retain production quality

Action approaches:

• Curative

• Preventive

© Zühlke 2015

Predictive Maintenance

Predictive Maintenance Explained | Nicolaj Kirchhof, Tim Speier May 2015

Is to predict when an equipment failure might occur and to prevent the failure by performing maintenance tasks

Monitor Condition

• Continuously record sensor information, e.g. force or acoustic emissions

• Enrich information, e.g. ERP data

Slide 8

Predict RUL

• Forecast health state

• Deduce remaining useful life and confidence

• Plan maintenance

Determine Health State

• Experience-based

• Model-based

• Data-driven

© Zühlke 2015

Experimental Setup


Machine

• 10400 RPM spindle speed

• 1555 mm/min feed rate

• 0.125 mm depth of Y cut

• 0.2 mm depth of Z cut

Signals

• Force (N) in X,Y,Z axis

• Vibration (g) in X,Y,Z axis

• Acoustic emission

• 50 KHz sample rate

May 2015 Slide 9

415 million x 7 sample data points

-20

-10

0

10

Forc

e Y

[N]

Source: https://www.phmsociety.org/competition/phm/10

© Zühlke 2015

ContinuousRecording

Feature Engineering

Model Training&

EvaluationPrediction Model Value

Prediction

Signal Processing Statistical Methods &

Enrichment

Regression &

Classification

Machine Learning

Predictive Maintenance Explained | Nicolaj Kirchhof, Tim Speier 10

General Approach for Supervised Learning Problems

© Zühlke 2015

Exploratory Data Analysis


-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

Force X Force Y Force Z Acc X Acc Y Acc Z

Normalized Difference Wear-Data

11

00.10.20.30.40.50.60.70.80.9

1

1 31 61 91 121 151 181 211 241 271 301

Normalized

0

50

100

150

Forc

e X

[N]

-0.2

0

0.2

Acce

lera

tion

X [g

]

0

20

40

60

80

100

120

140

160

1 31 61 91 121 151 181 211 241 271 301

Raw Data

© Zühlke 2015

Prediction Model

Slide 12Predictive Maintenance Explained | Nicolaj Kirchhof, Tim Speier

Wear Stage 1

Wear Stage 2

Wear Stage 3

01000010101001010010101011011010101001 1010101010100101001010100101010100101000 101010010100101010110010000101010010100101010110110101010010101010101010010101010100101010100101 1101010100101010101010100101001010100101010100101010101

Data Sets

• 2 training sets

• 1 validation set

Preprocessing

• 3 stages of wear

• 3 selected features

Hidden Markov Models

• Continuous emissions

• 2 components

© Zühlke 2015

Prediction Model


30

80

130

180

230

280

0 50 100 150 200 250 300

Wear Level Selection

45

95

145

195

1 31 61 91 121 151 181 211 241 271 301

Prediction

Min Error Predicted Max Error Wear

13

0

10

20

30

40

50

60

1 51 101 151 201 251 301

Input Features

© Zühlke 2015

Analysis Conclusion

Slide 14

Prediction model reflects measured tool wear:

• Mean error of 16% after 2 min (60 cuts)

• Mean error of 7% after 4 min (120 cuts)

You can only predict what you already know:

• Unexpected machine behaviour not considered

• Only one building block of a holistic system

Predictive Maintenance is also about

understanding data!


© Zühlke 2015

Tooling


Pictures: http://www.python.org/community/logos/http://developer.r-project.org/Logo/http://scikit-learn.org/

© Zühlke 2015

Tooling – Big and Managed


Pictures: http://aws.amazon.com/de/http://hortonworks.com/dehttp://azure.microsoft.com/de-de/

...

© Zühlke 2015

Picture: http://channel9.msdn.com/events/TechEd/Europe/2014/DBI-B218

May 2015Predictive Maintenance Explained | Nicolaj Kirchhof, Tim Speier

Azure ML Workflow

Slide 18

© Zühlke 2015

Nicolaj Kirchhof

[email protected]/Nicolaj_Kirchhof

Slide 19

Tim Speier

[email protected]/Tim_Speier

Zühlke. Empowering Ideas.

Instandhaltung im Kinderzimmer – Predictive Maintenance am Modell erklärt (Seacon 2015)

Software

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