The document outlines a project funded by the DBU aimed at developing a system for the automatic early detection and prediction of mould formation, particularly in cultural heritage sites. It emphasizes the increasing risk of mould due to climate change, highlights current detection challenges, and describes innovative methodologies using AI and image recognition for real-time monitoring. Key partners and planned field tests at significant locations like Xanten Cathedral are also mentioned.

1. System platform for automatic early
detection and prediction
of mould formation
Status: 2020-02-12
Funded by the DBU - File number: 35604/01
Project duration: 01.01.2020 - 31.12.2021

2. Project partners
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Hajuveda Heritage iXtronics GmbH
Address
Ruitzhof 26
52156 Monschau
In Dören Field 3
33100 Paderborn
Contact person Dr Hans-Jürgen Daams Michael Robrecht
E-mail hans.daams@hajuveda.solutions michael.robrecht@ixtronics.com
Phone +49 2472 6089909 +49 5251 41785-12
Fax +49 2472 6089946 +49 5251 41785-29
Role in the project Project coordinator Cooperation partner

3. Future risk of mould in Europe
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Risk folder for mould growth
harmfulsecure
Reference: /Leissner, J. and Fuhrmann, C. (2018)
/

4. Environmental relevance of the project
▪ "By the end of the century,
man-made climate change will
significantly increase the
development of mould and
thus the threat to the interior
fittings of churches and other
historically valuable cultural
assets.
Reference: /Leissner, J. and
Fuhrmann, C. (2018) /
Slide 4
Mould infestation on unknown
organ (from DBU 31242)
Mould growth on wooden cover at
the Barbara altar in Xanten
Cathedral

5. Environmental relevance of the project
▪ News about mould formation on cultural objects is becoming more and
more important in the specialist literature and the press
▪ There are increasing reports of mould infestation:
- Church Organs
- Altars
- Statues
- Paintings
- Curtains
/HAWK 2019/, /DBU final report AZ 31242-45 as well as AZ 30200-45 and the
currently running project DBU 34554_01/, /Kappel (2018) / and /Walde (2014) /)
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6. Environmental relevance of the project
▪ Possibilities for the early detection of mould infestation:
- Visual inspection for mould infestation by daily or weekly inspection tours
- Periodically performed microbiological analyses
- Simulation calculations and comparison with temperature and humidity
measurements carried out on site
▪ Disadvantages of all measures mentioned are:
- high costs and high effort
▪ Low staff capacity and tight budgets also limit the possibilities for mould
detection
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7. ▪ Need for an early warning system
- For continuous monitoring of objects
not yet affected
- For permanent monitoring of already
treated objects
- Applicable for typical mobile and
immobile objects in cultural sites
- But also for normal houses and
buildings
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▪ Recording of the mould-relevant
parameters:
- physical thermodynamic quantities in
the vicinity of the art objects
- Regular recording of the surface with
digital camera
- Use of artificial intelligence for early
detection of mould infestation
Environmental relevance of the project

8. Physical and microbiological basics
▪ Isopleth systems
- Germination time in days
- Growth rate in mm/day
- Combination of temperature and
relative humidity
▪ For the formation of mold are also
important:
- Duration of the humidity periods
- Water absorption capacity of the
substrate
Slide 8
Thermodynamic parameters determine
the isopleths for germination time and
growth rate of mould
(applies in near field of cultural assets)
Spore germination and mycelium growth
of Aspergillus restrictus and versicolor
according to /Smith, S (1982)/
moistandwarmhighestriskof
mould

9. Physical and microbiological basics
▪ Problem: The measurement of air temperature and relative humidity alone is
not sufficient
- The substrate must be considered
- The local climate directly at the object is decisive (influencing factors: heating,
air conditioning, air flow, people, sun, ...)
- The duration of the damp period or the dry period must be taken into account
- The formation of condensation on the object must be recorded
▪ The calculation of mould-influencing parameters with simulation tools such
as WUFI-Bio can only be carried out with special knowledge of building
physics
Slide 9

10. System platform for the automatic early detection and
prediction of mould formation
- Detects environmental conditions and
object surfaces
- Image recognition analyses mould
formation using artificial intelligence
methods
- Detects different types of damage e.g.
also cracks and water damage
- Stores the data in the cloud
- Automatically generates a warning
message to the user
- Correlation of environmental and
image data as a basis for the
preventive prediction of mould
formation
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▪ Objective: Results of the system platform can be understood without expert
knowledge and the system can be installed with minimal effort

11. Artificial intelligence with neural networks
▪ Attempt to map the brain of living beings and thus their pattern
recognition abilities in the computer
▪ Interconnection of individual artificial neurons into a network
▪ The number of network layers and neurons per layer results from the task
▪ Training the network by adjusting the weighting factors wi,j
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12. Image recognition with Convolutional Neural
Networks
- The input image is filtered in the detection section
in stages and broken down into its characteristics
from simple to complex
- Identification part extracts the properties to be
recognized from the image
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13. Web interface with data visualization
▪ Time data diagrams
- Temperatures
- Rel. air humidity
- Condensate formation
▪ Combined displays
- Scatter diagrams
- Bar charts
- Isoplethene
▪ Simple signal symbols
- traffic light
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14. Planned field tests and scientific advisory board
▪ Field Test User
- Xanten Cathedral
- Contact person: Cathedral master builder Schubert
- Test situation: Altars
- St.-Paulus-Cathedral Münster
- contact person:
- Test situation: Organ?
▪ Scientific Advisory Board
- Church Organs
- Thomas Löhter, Institute for Diagnostics and Conservation at Monuments, Dresden
- Mildew
- Judith Meider, Urbanus Laboratory, Düsseldorf
- AI
- Vesa Klumpp, Knowtion, Karlsruhe
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15. Thank you very much for supporting us!
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16. References
▪ Bibliography
- HAWK, 2019. climatic zone church: preventive conservation of the equipment,
Hildesheim: abstracts of the lectures Interdisciplinary conference 16 to 18 January 2019.
- Kappel, J., 2018. Xanten Cathedral has a mould problem - art treasures threatened,
https://www.kirche-und-leben.de/artikel/xantener-dom-hat-ein-schimmel-problem-
kunstschaetze-bedroht/.
- Leissner, J. and Fuhrmann. C., 2018. cultural heritage and climate change - are we at a
turning point?, see left: In: Cartaditalia IX (2018), pp. 220-234.
- Sedlbauer, K., 2001. prediction of mould formation, Stuttgart: dissertation.
- Sedlbauer, K. Z., 2003: Prediction model for mould formation in unsteady climate -
practical examples, see left: s.n.
- Walde, C. S. i., 2014. Organ in the cathedral is cleaned, Münster:
https://www.wn.de/Muenster/Kultur/2014/07/1645001-Orgel-im-Dom-wird-geputzt-Neuer-
Spieltisch.
- Smith, S. L.; Hill, S. T.: Influence of temperature and water activity on germination and
growth of Aspergillus restrictus and Aspergillus versicolor. Transactions of the British
Mycological Society Vol. 79 (1982), H. 3, S. 558 - 560.
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