Predictive Maintenance using MATLAB: Pattern Matching for … · Dr. Türck Ingenieurbüro Data...
Transcript of Predictive Maintenance using MATLAB: Pattern Matching for … · Dr. Türck Ingenieurbüro Data...
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Dr. Türck Ingenieurbüro
Data Science
Predictive Maintenance using MATLAB:
Pattern Matching for Time Series DataDr. Irina Ostapenko (Dr. Türck Ingenieurbüro GmbH),
Jessica Fisch (Daimler AG)
18.04.2018
Dr. Türck IngenieurbüroZwischen den Zahlen lesen
www.mercedes-benz.com
www.tuerck-optik.de
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Dr. Türck Ingenieurbüro
Data Science Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 2
Irina Ostapenko
Senior Data Scientist
Solutions and Algorithms
Kreuzbergstr. 37 Berlin 10965
Jessica Fisch
Mercedes-Benz Werk Mettingen
Digitale Fabrik Powertrain und Projekt Industrie 4.0
PT/TSD
010 – HPC M427
70546 Stuttgart
Mercedes-Benz
Collaboration partners
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Dr. Türck Ingenieurbüro
Data Science Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 3
Irina Ostapenko
Jessica Fisch
Collaboration partners
We provide
• Algorithms
• Signal Processing
• Measurement Systems Developing
Optical System Design
Mercedes-Benz
Focus:
• Digital Transformation
• Big Data
• IIoT
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Dr. Türck Ingenieurbüro
Data Science
Outline
1. Project introduction
2. Task description
3. Solution/Algorithm
4. Summary
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 4
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Dr. Türck Ingenieurbüro
Data Science
POWERTRAINFive modules form
the core of our cars
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 5
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Dr. Türck Ingenieurbüro
Data Science
The Powertrain production network is set up globally
with lead plant in Germany
Locations at a glance
Europe USA
China
Beijing
(Joint Venture with
BAIC Motor)
Decherd
(Cooperation with
Renault/Nissan)
Legend
100% Daimler Joint Venture
Majority Holding Cooperation
Berlin
Hamburg
Koelleda (MDC Power)
Arnstadt (MDC Technology)
Stuttgart
Rastatt
Jawor/Poland
Most/ Czech Republic
Maribor/Slovenia
Sebes and Cugir/Romania
(Star Transmission)
Kamenz (ACCUmotive)
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 6
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Dr. Türck Ingenieurbüro
Data Science
Motivation for Anomaly Detection in the Projekt „iLL“
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 7
Source: www.autem.de
Automatic Notification of the Deviation:PLC-Data today:
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Data properties in the context of Big Data
The 3 basic V's of Big Data:
• Velocity: Speed with which data is
generated and analyzed
• Volume: Amount of data that traditionally
can not be analyzed
• Variety: Data diversity refers to
unstructured data without a recognizable
context
The 2 additional V‘s:
• Validity: Ensuring data quality
• Value: measurable benefits from the data
Workshop zur Fabrik des Jahres | MB Werk Berlin | 05.03.18 8
Volume VarietyVelocity
sampling rate ≈100 ms
700 Variables6 TB/p.M.
Maschine
Machine State
PLC
Numerical Control
Big Data
2 GB/day
100 Machine =
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Dr. Türck Ingenieurbüro
Data Science
Machine
Benefits of „Intelligent Level-Learning“
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 9
Bearing
damaged !
Different cycle× Cycletime
× power
✓ Position
… …
--- Active power engine axis 1
--- Position axis 1
Normal cycle✓ Cycletime
✓ power
✓ Position
Error cycle× Cycletime
× power
× Position
Repair
Procurement
of spare
parts;
Downtime in
production
times
Create
maintenance
order for
unplanned
breakdown
Ensuring lean production by
controlling quality, cost and time
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Dr. Türck Ingenieurbüro
Data Science
Challenges
• About 700 parameters are continuously monitored in every production cycle
yielding 700 individual time-series of about 2500 samples each
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 10
Time, s
Pro
du
ctio
np
ara
me
terA
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Dr. Türck Ingenieurbüro
Data Science
Challenges
• About 700 parameters are continuously monitored in every production cycle
yielding 700 individual time-series of about 2500 samples each
• Different parameters show very different and elaborate features
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 11
Time, s
Pro
du
ctio
np
ara
me
terA
Time, s
Pro
du
ctio
np
ara
me
ter
B
Time, s
Pro
du
ctio
np
ara
me
ter
C
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Dr. Türck Ingenieurbüro
Data Science
Challenges
• About 700 parameters are continuously monitored in every production cycle
yielding 700 individual time-series of about 2500 samples each
• Different parameters show very different and elaborate features
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 12
Time, s
Pro
du
ctio
np
ara
me
terA
Time, s
Pro
du
ctio
np
ara
me
ter
B
Time, s
Pro
du
ctio
np
ara
me
ter
C
Task: Analyse these 700 time-series and find specific kinds of deviations
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Dr. Türck Ingenieurbüro
Data Science
Requirements for algorithm
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 13
Time, s
Pro
du
ctio
np
ara
me
ter
D
Time deviation
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Dr. Türck Ingenieurbüro
Data Science
Requirements for algorithm
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 14
Time, s
Pro
du
ctio
np
ara
me
ter
D
Time deviation
Time, s
Pro
du
ctio
np
ara
me
ter
E
Pattern deviation
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Dr. Türck Ingenieurbüro
Data Science
Requirements for algorithm
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 15
Time, s
Pro
du
ctio
np
ara
me
ter
D
Time deviation
Time, s
Pro
du
ctio
np
ara
me
ter
E
Pattern deviationmight not be critical is critical
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Dr. Türck Ingenieurbüro
Data Science
Requirements for algorithm
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 16
What the algorithm should do
• Time series analysis
• Find deviations from normal cycle and
• Distinguishing between time and pattern deviation What is normal?
Time, s
Pro
du
ctio
np
ara
me
ter
D
Time deviation
Time, s
Pro
du
ctio
np
ara
me
ter
E
Pattern deviationmight not be critical is critical
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Dr. Türck Ingenieurbüro
Data Science
Delays in production cycles
• Length of time-series varies from cycle to cycle
even for normal production
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 17
Cycle lengths over one day, s
Re
lative
Fre
qu
en
cy
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Dr. Türck Ingenieurbüro
Data Science
Delays in production cycles
• Length of time-series varies from cycle to cycle
even for normal production
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 18
Time, s
Pro
du
ctio
np
ara
me
ter
F
Time, s
Pro
du
ctio
np
ara
me
ter
F
Cycle lengths over one day, s
Re
lative
Fre
qu
en
cy
1 cycle
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Dr. Türck Ingenieurbüro
Data Science
Delays in production cycles
• Length of time-series varies from cycle to cycle
even for normal production
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 19
Time, s
Pro
du
ctio
np
ara
me
ter
F
Time, s
Pro
du
ctio
np
ara
me
ter
F
Cycle lengths over one day, s
Re
lative
Fre
qu
en
cy
2 cycle
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Dr. Türck Ingenieurbüro
Data Science
Delays in production cycles
• Length of time-series varies from cycle to cycle
even for normal production
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 20
Time, s
Pro
du
ctio
np
ara
me
ter
F
Time, s
Pro
du
ctio
np
ara
me
ter
F
Cycle lengths over one day, s
Re
lative
Fre
qu
en
cy
4 cycle
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Dr. Türck Ingenieurbüro
Data Science
Delays in production cycles
• Length of time-series varies from cycle to cycle
even for normal production
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 21
Time, s
Pro
du
ctio
np
ara
me
ter
F
Time, s
Pro
du
ctio
np
ara
me
ter
F
different delays
Cycle lengths over one day, s
Re
lative
Fre
qu
en
cy
Cycles from one day
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Dr. Türck Ingenieurbüro
Data Science
Delays in production cycles
• Length of time-series varies from cycle to cycle
even for normal production
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 22
Time, s
Pro
du
ctio
np
ara
me
ter
F
perfect match
after shift in time axis
Time, s
Pro
du
ctio
np
ara
me
ter
F
Time, s
Pro
du
ctio
np
ara
me
ter
F
different delays
Cycle lengths over one day, s
Re
lative
Fre
qu
en
cy
Normal cycles can be matched to one another
through shifting in time axis!
Cycles from one day
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Dr. Türck Ingenieurbüro
Data Science
Algorithm principle
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 23
no
rmal cyc
lete
stcyc
le
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Dr. Türck Ingenieurbüro
Data Science
Algorithm principle
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 24
t1 t2
no
rmal cyc
lete
stcyc
le
f1 f2
f3
• Cycle can be described as sequence of
features f1, f2, f3
• Each cycle can show some delays in
time t1, t2
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Dr. Türck Ingenieurbüro
Data Science
Algorithm principle
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 25
t1 t2
no
rmal cyc
lete
stcyc
le
f1 f2
f3
• Cycle can be described as sequence of
features f1, f2, f3
• Each cycle can show some delays in
time t1, t2
f1 f2
f3
• Automatic feature detection f1, f2, f3
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Dr. Türck Ingenieurbüro
Data Science
Algorithm principle
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 26
t1 t2
no
rmal cyc
lete
stcyc
le
f1 f2
f3
Δt3Δ t1 Δ t2
• Cycle can be described as sequence of
features f1, f2, f3
• Each cycle can show some delays in
time t1, t2
f1 f2
f3
• Pattern matching through shift of feature
along time axis (Δt2, Δt2, Δt3):
least square fit (tshift to minimize the Sum
of Residual Squares of two signals)
• Automatic feature detection f1, f2, f3
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Dr. Türck Ingenieurbüro
Data Science
Algorithm principle
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 27
Δt3Δ t1 Δ t2pattern deviation
time deviation
• Pattern matching through shift of feature
along time axis (Δt2, Δt2, Δt3):
minimization of SRS
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Dr. Türck Ingenieurbüro
Data Science
Algorithm principle
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 28
Δt3Δ t1 Δ t2pattern deviation
time deviation
• Pattern matching through shift of
feature along time axis (Δt1,
Δt2, Δt3): minimization of SRS
• Time and pattern deviation
for each feature are used
as characteristic numbers for test cycle
f1 f2 f3
Δt1 Δt2 Δt3 Time deviation
No No Yes Pattern deviation
Data reduction!
• Decription of a cycle as feature
sequence
• For each feature time and pattern
deviation can be calculated
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Dr. Türck Ingenieurbüro
Data Science
Automatic feature detection
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 29
Time series is split
• After a local extremum (maximum or minimum) or on a plateau
• After a given relative change
Time, s
Pro
du
ctio
np
ara
me
ter
C
Time, s
Pro
du
ctio
np
ara
me
ter
D
Time, s
Pro
du
ctio
np
ara
me
ter
E
4 features 7 features 14 features
Data reduction of time series from 2500 datapoints to sequence of max. 60 features (typically 10)!
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Dr. Türck Ingenieurbüro
Data Science
Algorithm implementation: machine learning approach in MATLAB
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 30
Training
Reference cycles ->
• Build „reference signal“ for each feature
• Limits for time and pattern deviation
Testing
Test cycles ->
• Comparison of each feature in reference signal
• Is time and pattern deviation within the limits?
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Dr. Türck Ingenieurbüro
Data Science
Create „reference signal“ for each produciton parameter
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 31
1. For all training cycles - matching to shortest cycle
2. Create „reference signal“ – mean over all matched reference cycles
Time, s
Pro
du
ctio
np
ara
me
ter
F
Almost perfect match after shifting
Cycles from one day
Time, s
Pro
du
ctio
np
ara
me
ter
F
Reference signal
Training
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Dr. Türck Ingenieurbüro
Data Science
Create „reference signal“ for each produciton parameter
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 32
1. For all training cycles - matching to shortest cycle
2. Create „reference signal“ – mean over all matched reference cycles
3. Possible pattern deviation - standard deviation over all matched reference cycles
Time, s
Pro
du
ctio
np
ara
me
ter
F
Almost perfect match after shifting
Cycles from one day
Time, s
Pro
du
ctio
np
ara
me
ter
F σ
Tolerance for pattern deviation
Time, s
Pro
du
ctio
np
ara
me
ter
F
Reference signal
Training
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Dr. Türck Ingenieurbüro
Data Science
Create „reference signal“ for each produciton parameter
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 33
1. Create „reference signal“ – mean over all matched reference cycles
2. Possible pattern deviation - standard deviation over all matched reference cycles, limits for SRS
3. Possible time deviation – maximal absolute shift from matched reference cycles
Nr. of feature
ma
xsh
ift,
s
Time, s
Pro
du
ctio
np
ara
me
ter
F σ
Tolerance for pattern deviation
Nr. of feature
ma
xS
RS
,
arb
.un
.
Possible time deviation
Possible pattern deviation
Training
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Dr. Türck Ingenieurbüro
Data Science
Testing: time and pattern deviation evaluation
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 34
error cycle
normal cycle
reference signal
Time, s
Pro
du
ctio
np
ara
me
ter
F
delay
a
a
a
Testing
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Dr. Türck Ingenieurbüro
Data Science
Testing: time and pattern deviation evaluation
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 35
error cycle
normal cycle
reference signal
Time, s
Pro
du
ctio
np
ara
me
ter
F
delay
a
a
a
Is time and pattern deviation
for this feature within the limits?
• Tolerance window (Δ t, SRS)
• Easy to spot a critical deviation
Testing
Time: max Shift (norm.)F
orm
:
max
SR
S (
norm
.)
feature a
0
☺
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Dr. Türck Ingenieurbüro
Data Science
Testing: time and pattern deviation evaluation
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 36
error cycle
normal cycle
reference signal
Time, s
Pro
du
ctio
np
ara
me
ter
F
delay
a
a
a
b
b
b
c
c
c d
d
dTesting
max Shift (norm.)
ma
xS
RS
(no
rm.)
feature d
0
max Shift (norm.)m
ax
SR
S (
no
rm.)
feature c
0
max Shift (norm.)
ma
xS
RS
(no
rm.)
feature b
0
max Shift (norm.)
ma
xS
RS
(no
rm.)
feature a
0
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Dr. Türck Ingenieurbüro
Data Science
Algorithm: Summary
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 37
1. Quantitative and qualitative description of produciton failure
2. Independent of signal form -> universally applicable to other applications or machines
3. Signal description with characteristic numbers, which are easy to interpret
4. Data reduction with a factor 250 without significant loss of information!
5. Easy control of production: recognition of critical errors and non-critical delays online
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Dr. Türck Ingenieurbüro
Data Science
Example of the added value
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 38
Results:
• Transparency of the process
• Deviation for each Signal
• Reason of Cycletime increase found
Time and Pattern deviation are recognized
Example: Change in code
normal
cycle
actual
cycle
deviation
0 50 100 150 200 250
Position Setpoint Spindle Axis W1
Valu
e
Time [s]
Englisch
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Dr. Türck Ingenieurbüro
Data Science
Summary
Algorithm using pattern matching for time series developed and implemented for production data
Why MATLAB?
• easy algorithm implementation
• existing solution for data import
• very good support and broad use in universities
MATLAB Products used:
• Signal Processing Toolbox
• Statistics and Machine Learning Toolbox
Outlook:• Parallel Computing Toolbox for performance improvement
Prototyp intelligent Level-Learning (iLL) has a new function for anomaly detection• Troubleshooting in case of failure (maintenance), Parts Planning, Influences on the quality
Optimization of repair time, spreaders amount, …
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 39
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Data Science
Thank you for your attention!
Predictive Maintenance using MATLAB: Pattern Matching for Time Series Data 40
Irina Ostapenko
Jessica Fisch
Mercedes-Benz
Picture Credits: © Can Stock Photo / AnatolyM, abluecup, maxxyustas