From Digital Twin toPredictive Maintenance

AI Monday Leipzig

Andreas HantschLeipzig, 25th November 2019

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 1

Challenge

Fig.: Jones (2018) [1], Data: Andrae & Edler (2015) [2]CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 2

Liquid-cooled hardware: Micro data centre (MDC)

• on the market since 2014• 20 own projects with approx. 150 MDCheatingI 16 single-family houseI 3 multi-family house (>200 flats)I 1 kindergarten

• Customer projects, such as Innogy SE

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 3

Liquid-cooled hardware: Data centre container (DCC)

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 4

Distributed cloud operation

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 5

Concept

However

• Much more complex system• Water in the data centre• Few data at the beginning of operation

Solution, based on earlier ideas of, e.g., [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]• Predictive maintenance system• Both model-based and data-based approaches for normal system’sbehaviour

• Machine learning for anomaly detection

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 6

Concept

However

• Much more complex system• Water in the data centre• Few data at the beginning of operation

Solution, based on earlier ideas of, e.g., [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]• Predictive maintenance system• Both model-based and data-based approaches for normal system’sbehaviour

• Machine learning for anomaly detection

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 6

Concept Predictive Maintenance Unit

Va

lid

ati

on

Start-up routine

Retrieving data via API

Pre-processing

Compare data with standard

Digital Twin

model-based

Machine Learning

data-based

Determine differences

Anomaly detection

algorithm

Post-processing

Shut-down routine

Tra

inin

g

Tim

e l

oo

p

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 7

Concept: First prototype – Thermohydraulic system

Demo in jupyter notebooks

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 8

Summary & Outlook

Summary

• Advantages due to combination of model and data approach• Digital twin already there (employed as FMU)• Automatic data analysis• Machine learning for subsequent model adaption

Outlook

• Improvement of anomaly detection• Set-up of online learning

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 9

Summary & Outlook

Summary

• Advantages due to combination of model and data approach• Digital twin already there (employed as FMU)• Automatic data analysis• Machine learning for subsequent model adaption

Outlook

• Improvement of anomaly detection• Set-up of online learning

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 9

Contact

Andreas HantschCLOUD&HEAT Technologies GmbH

Königsbrücker Straße 9601099 Dresden

Germanyandreas.hantsch@cloudandheat.com

https://cloudandheat.com

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 10

References I

[1] Nicolas Jones.How to stop data centres from gobbling up the world’s electricity.Nature, 561:163–166, 2018.

[2] Anders S. G. Andrae and Tomas Edler.On global electricity usage of communication technology: Trends to2030.Challenges, 6(1):117–157, 2015.

[3] R.A. Adey and D. Sriram.Applications of artificial intelligence to engineering problems.(OSTI ID: 5113597), 1 1987.

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 11

References II

[4] Ying Guo, Davood Dehestani, Jiaming Li, Josh Wall, Sam West, andSteven Su.Intelligent outlier detection for hvac system fault detection.Technical report, CSIRO ICT Centre, Sydney, Australia, 2012.

[5] Muhammad Waseem Ahmad, Monjur Mourshed, Baris Yuce, andYacine Rezgui.Computational intelligence techniques for HVAC systems: A review.Building Simulation, 9(4):359–398, mar 2016.

[6] Michael Wetter.A modelica-based model library for building energy and controlsystems.In Eleventh International IBPSA Conference, Glasgow, Scotland, July27-30, 2009, pages 652–659, 2009.

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 12

References III

[7] Jens Bastian, Christoph Clauß, Susann Wolf, and Peter Schneider.Master for co-simulation using fmi.In Modelica Conference, 2011.

[8] Felix Bünning, Roozbeh Sangi, and Dirk Müller.A modelica library for the agent-based control of building energysystems.Applied Energy, 193:52–59, may 2017.

[9] Andreas Hantsch.Vom Messwert zur Erkenntnis – Datenanalyse und künstlicheIntelligenz.In Treffpunkt Zukunft: Sensorik, Handwerkskammer Dresden, 2019.

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 13

References IV

[10] Andreas Hantsch and Sabine Döge.Assessment of micro-organism growth risk on filters with machinelearning.In Clima 2019 – 13th REHVA World Congress, Bucharest, Romania,26.–29. May 2019, 2019.

[11] Andreas Hantsch.Machine learning with little data - from digital twin to predictivemaintenance.In PyCon.DE & PyData Berlin, 07.-09.10.2019, 2019.

[12] Andreas C. Müller and Sarah Guido.Introduction to Machine Learning with Python: A Guide for DataScientists.O’Reilly Media Inc., Sebastopol, 2016.

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 14

References V

[13] Aurélien Géron.Hands-On Machine Learning with Scikit-Learn and TensorFlow:Concepts, Tools, and Techniques to build intelligent systems.O’Reilly Media Inc., Sebastopol, 2017.

CLOUD&HEAT Technologies GmbH. We build the most energy e�cient data centers. Worldwide. https://cloudandheat.com 15