Predictive Maintenancefor Transport SystemsEmploying Model Ensembles for Online State Detection

Jan Zenisek, Michael Affenzeller, Josef Wolfartsberger,

Mathias Silmbroth, Christoph Sievi, Aziz Huskic, Herbert Jodlbauer

Smart Factory Lab, IWB 2014 – 2020

Heuristic and Evolutionary Algorithms Laboratory (HEAL)

University of Applied Sciences Upper Austria

Maintenance Today…

Predictive Maintenance (PdM)

• Maintenance levels: Corrective → Preventive → Predictive

• Goal: Smart scheduling of maintenance

• How: Condition-monitoring via sensors and data analysis

• Current application areas• Production plants (e.g. semi-conductors, injection moulds)

• Transport systems (e.g. railway tracks, car cambelts, engines)

Predictive Maintenance (PdM)

• Detection Variants• Anomaly detection• Breakdown prediction (Remaining Useful Lifetime)• Quality loss prediction

• Effects of improved predictability• Shortens downtimes → increases productivity (profit)• Anticipates potential failure → increases overall safety • Avoids unnecessary replacements → mitigates costs (time, material, energy)

• Improves sustainability performance

A PdM Setup

• PdM is linked with…• Industry 4.0

• Cyber Physical Systems (CPS)

• Internet of Things (IoT)

• Cloud Computing

• Big Data Analytics

• Machine Learning

Example Maintenance Problem:

Turbofan Degradation

Picture by Maarten Visser

Turbofan Degradation (Training)

Unit Setting Sensor 1 Sensor 2 … Sensor 21 Remaining Useful Life (RUL)

42 0.007 1597.23 1419.03 520.05

42 0.008 1605.44 1432.52 519.77

42 0.007 1603.46 1424.41 519.82

42 0.005 1595.16 1426.33 520.08

42 0.005 1592.14 1427.27 519.53

42 0.005 1602.38 1422.78 519.79

42 0.006 1596.17 1428.01 519.58

42 0.006 1599.22 1425.95 520.04

42 0.007 1602.36 1425.77 519.57

42 0.007 1601.41 1427.27 520.08 0

Turbofan Degradation (Test)

Unit Setting Sensor 1 Sensor 2 … Sensor 21 Remaining Useful Life (RUL)

42 0.008 1596.82 1410.09 520.46

42 0.007 1589.54 1420.37 520.05

42 0.007 1590.65 1418.08 519.77

42 0.006 1594.20 1417.31 519.82

42 0.006 1592.22 1423.48 520.08

42 0.006 1600.54 1421.09 519.53

42 0.005 1598.96 1416.76 519.79

42 0.005 1597.03 1408.09 519.58

42 0.007 1596.72 1422.37 520.04

42 0.008 1593.83 1414.72 519.57 ??? (22)

Benchmark data set• 100 units (run-to-failure sets)• 150-250 rows per unit

Per row• 3 operational settings• 21 sensor measurements

Major difficulties:• Damage propagation starting

point is not known!• RUL only provided per Run-To-

Failure set, not per row

Thresholds vs. Prediction Models

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𝑣𝑎𝑟𝑠 𝑠1, 𝑠2…𝑠𝑛 ,

𝑠𝑡−1, 𝑠𝑡−2, 𝑐𝑜𝑛𝑠𝑡𝑠, …

+,−,∗, 𝑎𝑣𝑔…

𝑠𝑖𝑛 𝑠 , 𝑡𝑎𝑛 𝑠 , 𝑠, …

𝑓 𝑠 = 𝑠7 ∙ 1.42 + 𝑠9…

𝑓 𝑠 =𝑠5𝑠1

− 𝑠6

𝑓 𝑠 = ⋯

Machine Learning

• Problem: Symbolic Regression

• Algorithm: Genetic Programming (GP)

• Solution: Ensemble Models

𝒇 𝒔 = ?

𝒇 𝒔 = 𝒍𝒐𝒈 𝒔𝟒 ∙ 1.42𝐸 + 001 +𝒔𝟖 ∙ 1.23𝐸 + 001

𝒔𝟕 ∙ 1.11𝐸 + 001…

GP

HeuristicLab

• Open Source Optimization Environment

• Application areas• Data based modeling and analysis• Production planning and logistics optimization• Simulation-based optimization

• Large sets of algorithms and problem types• Rich graphical user interface• Distributed computing• Basis of many research projects and publications• Continuous development since 2002

http://dev.heuristiclab.com/download

Modeling Process in

Benchmarking Results

• Modeling• Algorithm: GP with Offspring Selection GA (OSGA)• Ensemble with 10 Symbolic Regression Solutions

• Prediction Quality• OSGA Training on last 50% 20.9965 MAE• OSGA Training on last 25% 16.6898 MAE

• RF Training on last 50% 22.0886 MAE• RF Training on last 25% 43.5955 MAE

• Current models tend to be very „cautious“

Real-Time System State Detection

Tunable controller for

• stable-state detection(in case of a winning ensemble)

• concept change and drift detection(if no ensemble exceeds threshold)

Conclusion & Outlook

• Promising results on synthetic data sets• Prediction of Remaining Useful Lifecycles (RUL)• Real-Time state and concept change detection

• Prototypical DSS for Predictive Maintenance

• Critical aspects for success• Data set quality and supervised training phase• Algorithm tuning• Datastream evaluation effort

Conclusion & Outlook

• PdM has large potential to improve productivityand sustainability of transport and production

• PdM is trending!• Connected Vehicles• Smart Production (Industry 4.0)

• Next steps• Tests on real-world data• Algorithmic adaptions and enhancements• Prototypical setups

Thank you, Questions?Predictive Maintenance for Transport SystemsEmploying Model Ensembles for Online State Detection

Jan Zenisek, Michael Affenzeller, Josef Wolfartsberger,

Mathias Silmbroth, Christoph Sievi, Aziz Huskic, Herbert Jodlbauer

Smart Factory Lab, IWB 2014 – 2020 / www.smartfactorylab.at / jan.zenisek@fh-hagenberg.at

Heuristic and Evolutionary Algorithms Lab (HEAL) / Softwarepark 11, 4232 Hagenberg, Austria / http://heal.heuristiclab.at

University of Applied Sciences Upper Austria