https://orcid.org/0000-0001-6546-8577
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Abstract
Silk is highly susceptible to fade, often resulting in colour loss to the fabric even when displayed in controlled museum environments. This study applies the engineering-inspired Prognostics and Health Management (PHM) approach to assess the remaining useful life (RUL) of silk fabric in a museum environment, focussing on predicting colour fade as an indicator of degradation. A novel mathematical model is developed to forecast cumulative colour fade of silk upholstery exhibited at the Great Gallery in The Wallace Collection, London. Unlike traditional accelerated aging methods, this model utilises naturally aged samples to estimate the rate of colour change over time. The main contribution of this work lies in the methodology and computational framework for model development using environmental data and conditions of silk in the museum environment. The proposed PHM methodology allows for continuous modifications of the colour fade model to improve accuracy by deploying new data from different silk collections and environments. The article demonstrates a model-based approach for informed decision making in museums regarding display and storage of silk upholstery.
Resumen
“Un enfoque de pronóstico y de gestión de salud para determinar el estado de la seda en los ambientes museísticos utilizando la decoloración”
La seda es muy susceptible a la decoloración lo que a menudo provoca la pérdida de color del tejido incluso cuando se expone en entornos museísticos controlados. Centrándose en la predicción de la pérdida de color como indicador de degradación, en este estudio se aplica un enfoque inspirado en la ingeniería para evaluar la vida útil restante (RUL) del tejido de seda en los ambientes museísticos, la gestión del pronóstico y la salud (PHM). Se desarrolla un nuevo modelo matemático para predecir la pérdida acumulada de color de la tapicería de seda expuesta en la Gran Galería de la Wallace Collection de Londres. A diferencia de los métodos tradicionales de envejecimiento acelerado, este modelo utiliza muestras envejecidas de forma natural para estimar el índice de cambio de color a lo largo del tiempo. La principal aportación de este trabajo radica en la metodología y el marco computacional usado para el desarrollo del modelo a partir de los datos ambientales y las condiciones de la seda en el entorno del museo. Mediante el uso de nuevos datos procedentes de diferentes colecciones y ambientes de la seda, la metodología propuesta, PHM, permite realizar continuas modificaciones del modelo de decoloración para así mejorar su precisión. El artículo demuestra un enfoque basado en modelos para adoptar decisiones informadas en relación con la exposición y el almacenamiento de tapicerías de seda en museos.
الملخص
“ تحديد حالة الحرير المعروض في البيئة المتحفية عبر اتباع نهج التكهنات وإدارة الصحة و استخدام نموذج تلاشي الألوان ”
الحرير قابل للتلاشي بدرجة عالية، مما يؤدي غالباً إلى فقدان لون النسيج ولوعُرض في بيئات المتاحف المُحكمة. تهدف هذه الدراسة إلى استخدام منهج التكهنات وإدارة الصحة (PHM) المستوحى من مجال الهندسة لتقييم العمر الإنتاجي المتبقي/الافتراضي (RUL) لنسيج الحرير في بيئة المتحف، مع التركيز على توقع تلاشي الألوان كمؤشر على التدهور. يتم تطوير نموذج رياضي جديد لتوقع تلاشي الألوان التراكمي لتنجيد الحرير المعروض في الصالة الكبرى (Great Gallery) من مجموعة والاس (ًWallace Collection) في لندن. على عكس الأساليب التقليدية للتقادم الصناعي المعجل، يستخدم هذا النموذج عينات معمرة بشكل طبيعي لتقدير معدل تغير اللون مع مرور الوقت. يكمن المساهم الرئيسي لهذا العمل في المنهجية والإطار الحسابي لتطوير النموذج باستخدام البيانات البيئية وحالة الحرير في بيئة المتحف المحكمة. تتيح المنهجية PHM المقترحة للتعديلات المستمرة على نموذج تلاشي الألوان لتحسين الدقة عن طريق استخدام بيانات جديدة من مجموعات الحرير والبيئات المختلفة. يُظهر المقال نهجاً قائماً على النموذج لاتخاذ القرارات المستندة على البيانات بشأن عرض وتخزين تنجيد الحرير في المتاحف.
Resumo
“Uma abordagem de prognóstico e de gerenciamento da saúde utilizando desbotamento de cor para determinar o estado da seda em um ambiente de exposição de museu”
Seda é altamente suscetível a desbotar, resultando com frequência em perda de cor do tecido mesmo quando exposto em ambientes controlados de museus. Este estudo aplica a abordagem Prognósticos e Gestão da Saúde (Prognostics and Health Management—PHM), inspirado na engenharia para avaliar a vida útil remanescente (remaining useful life—RUL) do tecido de seda em ambiente de museu, focando em prever o desbotamento de cor como um indicador de degradação. Um novo modelo matemático foi desenvolvido para prever o esmaecimento cumulativo da cor de estofamento de seda exibido na Great Gallery na Wallace Collection, em Londres. Ao contrário de métodos tradicionais de envelhecimento acelerado, este modelo utiliza amostras envelhecidas naturalmente para estimar o grau de mudança da cor ao longo do tempo. A principal contribuição deste trabalho está na metodologia e no esquema computacional para desenvolvimento do modelo, utilizando dados ambientais e condições da seda no ambiente do museu. A metodologia proposta PHM permite modificações contínuas do modelo de desbotamento de cor para melhorar a precisão ao utilizar novos dados de diferentes coleções de seda e de ambientes. O artigo apresenta uma abordagem baseada em modelos para tomadas de decisão em museus fundamentadas em relação à exposição e guarda de estofamento de seda.
摘要
“一种用褪色来预判博物馆展示环境中的丝绸健康状况的管理方法”
丝绸极易褪色，即便在受控的博物馆环境中展示也往往褪色。本研究采用了受工程学启发的预测与健康管理（PHM）方法来评估博物馆环境中丝织品的剩余使用寿命（RUL），其重点在于预测作为降解指标的褪色情况。我们开发了一种新颖的数学模型，用于预测伦敦华莱士收藏馆（The Wallace Collection）大艺廊展出的丝绸装饰品的累积褪色情况。与传统的加速老化方法不同，该模型利用了自然老化样本来估算颜色随时间改变的速率。本研究的主要贡献在于基于博物馆环境数据和丝绸状况的建模方法和计算框架。所提出的PHM方法支持对褪色模型进行持续修改，并通过来自不同丝绸藏品和环境的新数据来提高准确性。本文展示了一种基于模型的方法，便于博物馆在丝绸装饰品的展示和储存方面做出明智的决策。
Acknowledgements
The authors extend their heartfelt gratitude to The Wallace Collection for generously providing invaluable environmental sensor data and granting access to the ‘Shrewsbury Set’. Additionally, we wish to express our appreciation to Jürgen Huber for his invaluable contributions, engaging in insightful discussions surrounding the historical and economic implications of conserving silk within museum collections.
Notes
1 See, for example, Rui Dang et al., ‘Spectral Damage Model for Lighting Paper and Silk in Museum’, Journal of Cultural Heritage 45 (2020): 249–53, https://www.sciencedirect.com/science/article/pii/S1296207419307228 (accessed 10 February 2024).
2 Cf. Patricia Annis, ‘Silk Durability and Degradation’, in Understanding and Improving the Durability of Textiles (Oxford: Woodhead/Elsevier, 2012), 205–31.
3 Cf. Margaret J. Smith and Karen Thompson, ‘Forensic Analysis of Textile Degradation and Natural Damage’, in Forensic Textile Science, ed. Debra Carr (Oxford: Woodhead/Elsevier, 2017), 41–69.
4 See, for example, Samaneh Sharif and Vahid Esmaeili, ‘Effects of Temperature and Relative Humidity on Permanence of Buyid Silk’, Journal of Cultural Heritage 27 (2017): 72–9.
5 Cf. Tim Padfield, The Preservation Index and the Time Weighted Preservation Index, https://www.conservationphysics.org/twpi/twpi_01.html (accessed 10 February 2024).
6 Cf. Smith and Thompson, ‘Forensic Analysis of Textile Degradation and Natural Damage’, 41–2.
7 See, for example, Francisco Vilaplana et al., ‘Analytical Markers for Silk Degradation: Comparing Historic Silk and Silk Artificially Aged in Different Environments’, Analytical and Bioanalytical Chemistry 407 (2015): 1433–49.
8 Beata Gutarowska et al., ‘Historical Textiles—A Review of Microbial Deterioration Analysis and Disinfection Methods’, Textile Research Journal 87, no. 19 (2017): 2388–406; Paul Garside et al., ‘An Investigation of Weighted and Degraded Silk by Complementary Microscopy Techniques’, Heritage Science 11 (2014): 15–21. See, for example, Julio M. del Hoyo-Meléndez and Marion F. Mecklenburg, ‘Micro-Fading Spectrometry: A Tool for Real-Time Assessment of the Light-Fastness of Dye/Textile Systems’, Fibers and Polymers 13 (2012): 1079–85.
9 Cf. Naomi Luxford, David Thickett, and Paul Wyeth, ‘Preserving Silk: Reassessing Deterioration Factors for Historic Silk Artefacts’, in Natural Fibres in Australasia: Proceedings of the Combined (NZ and AUS) Conference of The Textile Institute, Dunedin 15–17 April 2009, ed. Cheryl Anne Wilson and Raechel Laing (Dunedin, New Zealand: The Textile Institute 2009), 151–7; cf. for example, Naomi Luxford and David Thickett, ‘Designing Accelerated Ageing Experiments to Study Silk Deterioration in Historic Houses’, Journal of the Institute of Conservation 34, no. 1 (2011): 115–27.
10 Cf. Roman Kozlowski, ‘Collection Environments and Evidence-Based Decision-Making’, Conservation Perspectives, The GCI Newsletter (Fall 2018): 13–5, https://www.getty.edu/conservation/publications_resources/newsletters/pdf/v33n2.pdf (accessed 12 February 2024).
11 Cf., for example, Foekje Boersma et al., ‘The Managing Collection Environments Initiative: A Holistic Approach’, Conservation Perspectives, The GCI Newsletter (Fall 2018): 10–2, https://www.getty.edu/conservation/publications_resources/newsletters/pdf/v33n2.pdf (accessed 12 February 2024).
12 Cf. for example, Jaroslav Valach et al., ‘Everything Is Data—Overview of Modular System of Sensors for Museum Environment’, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences—ISPRS Archives 40, no. 5W7 (2015): 439–42.
13 Cf. Kozlowski, ‘Collection Environments and Evidence-Based Decision-Making’.
14 See, for example, Bo Sun et al., ‘Benefits Analysis of Prognostics in Systems’, Prognostics and System Health Management Conference, Macao, China (2010): 1–8, https://ieeexplore.ieee.org/document/5413503 (accessed 12 February 2024).
15 Cf. for example, Cheng Lu et al., ‘A Prognostic Model for Overall Survival of Patients with Early-Stage Non-Small Cell Lung Cancer: A Multicentre, Retrospective Study’, The Lancet Digital Health 2, no. 11 (2020): e594–e606, [online] http://www.sciencedirect.com/science/article/pii/S2589750020302259 (accessed 12 February 2024).
16 See, for example , Jie Yang et al., ‘Elevated Cardiac Biomarkers May be Effective Prognostic Predictors for Patients with COVID-19: A Multicenter, Observational Study’, The American Journal of Emergency Medicine 39 (2021): 34–41, https://doi.org/10.1016/j.ajem.2020.10.013 (accessed 12 February 2024).
17 See, for example, Gibeom Kim et al., ‘Application of Particle Filtering for Prognostics with Measurement Uncertainty in Nuclear Power Plants’, Nuclear Engineering and Technology 50, no. 8 (2018): 1314–23, http://www.sciencedirect.com/science/article/pii/S1738573318302742 (accessed 12 February 2024).
18 See, for example, Abhinav Saxena et al., ‘Metrics for Evaluating Performance of Prognostic Techniques’ (presented at 2008 International Conference on Prognostics and Health Management, Denver, CO, 2008): 1–17, https://ieeexplore.ieee.org/document/4711436 (accessed 12 February 2024).
19 See, for example, The Wallace Collection (no date), The Collection, https://www.wallacecollection.org/collection/ (accessed 15 July 2021). Part of the ‘Shrewsbury Set’ is shown here: https://wallacelive.wallacecollection.org:443/eMP/eMuseumPlus?service=ExternalInterface&module=collection&objectId=63397&viewType=detailView (accessed 12 February 2024).
20 See for example, Piero Baraldi et al., ‘Model-Based and Data-Driven Prognostics under Different Available Information’, Probabilistic Engineering Mechanics 32 (2013): 66–79.
21 See, for example, Dennis Hoffman, ‘Prognostics and Health Management (PHM )/Condition Based Maintenance’, IEEE Reliability Society 2007 Annual Technology Report (2007): 1–7, https://rs.ieee.org/images/files/Publications/2007/2007-15.pdf (accessed 12 February 2024).
22 Cf. Sun et al., ‘Benefits Analysis of Prognostics in Systems’, 1.
23 See, for example, Behnoush Rezaeianjouybari and Yi Shang, ‘Deep Learning for Prognostics and Health Management: State of the Art, Challenges, and Opportunities’, Measurement 163 (2020): 107929, https://doi.org/10.1016/j.measurement.2020.107929 (accessed 12 February 2024).
24 See, for example, Huiguo Zhang, Rui Kang, and Michael Pecht, ‘A Hybrid Prognostics and Health Management Approach for Condition-Based Maintenance’ (presented at the 2009 IEEE International Conference on Industrial Engineering and Engineering Management, Hong Kong, China, 2009): 1165–9, https://doi.org/10.1109/IEEM.2009.5372976 (accessed 12 February 2024).
25 See, for example, Olga Fink et al., ‘Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications’, Engineering Applications of Artificial Intelligence 92 (2020), https://doi.org/10.1016/j.engappai.2020.103678 (accessed 12 February 2024).
26 See, for example, Gregory W. Vogl, Brian A. Weiss, and Moneer Helu, ‘A Review of Diagnostic and Prognostic Capabilities and Best Practices for Manufacturing’, Journal of Intelligent Manufacturing 30, no. 1 (2019): 79–95.
27 Cf. Baraldi et al., ‘Model-Based and Data-Driven Prognostics under Different Available Information’.
28 See Zhang, Kang, and Pecht, ‘A Hybrid Prognostics and Health Management Approach for Condition-Based Maintenance’.
29 See, for example, Chao Hu et al., ‘Ensemble of Data-Driven Prognostic Algorithms for Robust Prediction of Remaining Useful Life’, Reliability Engineering & System Safety 103 (2012): 120–35.
30 See, for example, Xiao-Sheng Si et al., ‘Remaining Useful Life Estimation—A Review on the Statistical Data Driven Approaches’, European Journal of Operational Research 213, no. 1 (2011): 1–14.
31 See Vogl, Weiss, and Helu, ‘A Review of Diagnostic and Prognostic Capabilities and Best Practices for Manufacturing’.
32 See, for example, Kwok L. Tsui et al., ‘Prognostics and Health Management: A Review on Data Driven Approaches’, Mathematical Problems in Engineering 2015 (2015): 793161, https://doi.org/10.1155/2015/793161 (accessed 12 February 2024).
33 See, for example, Jay Lee et al., ‘Introduction to Data-Driven Methodologies for Prognostics and Health Management’, Probabilistic Prognostics and Health Management of Energy Systems (2017): 9–32, https://doi.org/10.1007/978-3-319-55852-3_2 (accessed 12 February 2024).
34 Si et al., ‘Remaining Useful Life Estimation’.
35 Tsui et al., ‘Prognostics and Health Management’.
36 See, for example, Hidehiko Murata, Kotaro Saitoh, and Yasuhiko Sumida, ‘True Color Imagery Rendering for Himawari-8 with a Color Reproduction Approach Based on the CIE XYZ Color System’, Journal of the Meteorological Society of Japan 96B (2018): 211–38.
37 See, for example, Noor Ibraheem et al., ‘Understanding Color Models: A Review’, ARPN Journal of Science and Technology 2, no. 3 (2012): 265–75.
38 See, for example, TES Electrical Electronic Corporation, TES 136 Chronometer Product Information, 2020, http://www.tes.com.tw/en/product_detail.asp?seq=363 (accessed 13 March 2024).
39 International Ciommiion on Illumination (CIE), ‘Colorimetry—Part 2: CIE Standard Illuminants’, http://cie.co.at/publications/colorimetry-part-2-cie-standard-illuminants (accessed 14 November 2022).
40 Tsui et al., ‘Prognostics and Health Management’.
41 Sun-X (n.d), ‘Conservation: Window Films and Blinds’, https://www.sun-x.co.uk/conservation (accessed 17 June 2021).
42 Cf. Susan M. Bradley, ‘Do Objects have a Finite Lifetime?’, in Care of Collections: Leicester Readers in Museum Studies (Oxford: Routledge, 1994), 51–8.
43 Cf. J.N. Chakraborty, Fundamental and Practices in Colouration of Textiles (New Delhi; CRC Press, 2015), 453.
44 Cf. Cheunsoon Ahn et al., ‘Thermal Degradation of Natural Dyes and their Analysis using HPLC-DAD-MS’, Fashion and Textiles 1, no. 22 (2014), https://doi.org/10.1186/s40691-014-0022-5 (accessed 13 March 2024).
45 Cf. for example, Paul Garside and Emma Richardson, Conservation Science: Heritage Materials (Cambridge: RSC Publishing, 2006), 331–87; David Saunders and Jo Kirby, ‘National Gallery Technical Bulletin: The Effect of Relative Humidity on Artists’ Pigments’, National Gallery Technical Bulletin 25 (2004): 62–72.
46 See, for example, Athina Vasileiadou, Ioannis Karapanagiotis, and Anastasia Zotou, ‘UV-Induced Degradation of Wool and Silk Dyed with Shellfish Purple’, Dyes and Pigments 168 (April 2019): 317–26.
47 See, for example, Hongling Liu et al., ‘Secondary Structure Transformation and Mechanical Properties of Silk Fibres by Ultraviolet Irradiation and Water’, Textile Research Journal 89, no. 14 (2019): 2802–12, https://doi.org/10.1177/0040517518803788 (accessed 13 March 2024).
48 Cf. Harry Spaling, ‘Cumulative Effects Assessment: Concepts and Principles’, Impact Assessment 12, no. 3 (1994): 231–51.
49 Cf. Robert Eliason and Terence McMahon, ‘Temperature Effect on Reaction Rates’, Journal of Chemical Education 58, no. 4 (1981): 354, https://doi.org/10.1021/ed058p354.1 (accessed 13 March 2024).
50 See, for example, Eliason and McMahon, ‘Temperature Effect on Reaction Rates’.
51 See, for example, David Hukins, Aziza Mahomed, and Stephen Kukureka, ‘Accelerated Aging for Testing Polymeric Biomaterials and Medical Devices’, Medical Engineering & Physics 30, no. 10 (2008): 1270–4, https://doi.org/10.1016/j.medengphy.2008.06.001 (accessed 13 March 2024).
52 Cf. Asim Kumar Roy Choudhury, ‘3-Colour-Difference Assessment’, in Principles of Colour and Appearance Measurement (Oxford: Woodhead Publishing, 2015), 55–116, https://doi.org/10.1533/9781782423881.55 (accessed 13 March 2024).
53 Cf. Eric May and Mark Jones, Conservation Science: Heritage Materials (Cambridge: RSC Publishing, 2006), 73–83; Sharif and Esmaeili, ‘Effects of Temperature and Relative Humidity on Permanence of Buyid Silk’.
Additional information
Notes on contributors
Aditi Rawal
Aditi Rawal is an Academic Portfolio Lead for Computer Science at the University of Greenwich, London. She has over 21 years experience in systems analysis and design, including software development methodologies. She obtained her PhD in 2021 with her research, ‘Diagnostic and Prognostic Methodology for Monitoring Silk Fade in a Museum Environment’ from the University of Greenwich. She developed a non-invasive and non-destructive methodology for condition-based monitoring of silk. Her research interests revolve around system analysis methodologies, data analysis and prognostic modelling for historic artefacts in museum settings. She is a Senior Fellow of the Higher Education Academy (SFHEA) and is a member of the British Computing Society (BCS).
Peter Mason
Peter Mason is a civil engineer, now retired and until recently a visiting professor at the universities of Greenwich and Surrey. He has worked on many heritage structures, including the Cutty Sark, SS Great Britain and the Clifton Suspension Bridge. Throughout his long and varied career as an engineering designer, he has paid particular attention to the predictive mathematical modelling of degradation so that quantified correlations with environmental conditions could be found, leading to planned-for maintenance and extended useful life.
Stoyan Stoyanov
Stoyan Stoyanov is a Reader in Computational Engineering and Head of the Computational Mechanics and Reliability Group at the School of Computing and Mathematical Sciences. His research interests include the development and application of modelling and simulation tools for numerical analysis of the performance, degradation and reliability of materials, with a focus on mechatronics physical systems, physics-of-failure modelling, computational intelligence for data-driven and prognostic modelling, and heritage digital conservation. He has published over 130 peer-reviewed articles.
Chris Bailey
Chris Bailey earned a PhD in computational modelling from Thames Polytechnic, UK in 1988. He served as a Research Fellow at Carnegie Mellon University from 1988 to 1991, and later joined the University of Greenwich, London, where he became a professor in computational mechanics and reliability from 2001 to 2022. He currently serves as Professor and Centre Director at Arizona State University, USA. His research focusses on multi-physics modelling, engineering reliability, optimisation, and prognostics and health management. With over 400 publications, he is a Senior Member of IEEE and a Member of the IEEE Electronic Packaging Society Board of Governors.
Jurgen Huber
Jurgen Huber is Senior Furniture Conservator at The Wallace Collection (ACR), where he is responsible for preventive and interventive conservation of furniture and related works. After completing a 3-year apprenticeship in cabinetmaking in 1987, and following the journeyman tradition in Germany and France, he gained a ‘Meister im Tischlerhandwerk’ in 1992. Having learned a very practical approach to restoration, he completed a 3-year conservation course, gaining a postgraduate diploma in conservation studies in 1998. Since then he has had the opportunity to work for various other institutions abroad and in the UK.