{"id":40738,"date":"2026-01-30T12:13:26","date_gmt":"2026-01-30T11:13:26","guid":{"rendered":"https:\/\/tsisl.es\/?post_type=project&p=40738"},"modified":"2026-01-30T12:14:20","modified_gmt":"2026-01-30T11:14:20","slug":"project-nois-ia","status":"publish","type":"project","link":"https:\/\/tsisl.es\/en\/project\/project-nois-ia\/","title":{"rendered":"Nosi-IA project"},"content":{"rendered":"

[et_pb_section fb_built=”1″ custom_padding_last_edited=”on|tablet” admin_label=”Contact” _builder_version=”4.27.4″ use_background_color_gradient=”on” background_color_gradient_stops=”#041e42 10%|rgba(4,30,66,0.85) 100%” background_color_gradient_start=”#041e42″ background_color_gradient_start_position=”10%” background_color_gradient_end=”rgba(4,30,66,0.85)” background_enable_image=”off” custom_margin=”|||” custom_padding=”30px|0|30px||true|false” custom_padding_tablet=”30px||30px||true|false” custom_padding_phone=”10px||50px||false|false” top_divider_color=”#041E42″ top_divider_height=”200px” bottom_divider_style=”arrow2″ bottom_divider_color=”#FFFFFF” da_disable_devices=”off|off|off” locked=”off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” header_font=”Albert Sans||||||||” header_2_text_color=”#64A70B” header_2_font_size=”40px” header_3_text_color=”#64A70B” text_orientation=”center” background_layout=”dark” inline_fonts=”Krub,Albert Sans” global_colors_info=”{}”]<\/p>\n

Project References<\/span><\/h4>\n

Nois-IA project<\/span><\/strong><\/h1>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.23.2″ _module_preset=”default” custom_padding=”||22px|||” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”|auto|-55px|auto|false|false” custom_padding=”||25px||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#978C87″ global_colors_info=”{}”]<\/p>\n

Nois-IA Project – Artificial Intelligence for Underwater Noise Prediction<\/strong><\/p>\n

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El proyecto WILLOW<\/strong> busca desarrollar un Sistema de Monitorizaci\u00f3n de Salud Estructural<\/strong> (SHM) innovador, que utilice modelos f\u00edsicos y m\u00e9todos de inteligencia artificial para evaluar la vida \u00fatil de infraestructuras e\u00f3licas. Esto contribuir\u00e1 a reducir el Costo Nivelado de Energ\u00eda (LCOE) y aumentar la Producci\u00f3n Anual de Energ\u00eda (AEP).<\/p>\n

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TSI refuerza su estrategia de innovaci\u00f3n con el proyecto Nois-IA<\/strong>, una iniciativa piloto de seis meses que marca el inicio de nuestra incursi\u00f3n en el desarrollo de soluciones basadas en Inteligencia Artificial (IA)<\/strong>. Este proyecto, liderado por el equipo de I+D+i, explora la aplicaci\u00f3n de algoritmos inteligentes para la detecci\u00f3n y predicci\u00f3n de la cavitaci\u00f3n y del Ruido Radiado al Agua (URN)<\/strong> en diferentes condiciones. <\/span><\/p>\n

Aunque es un proyecto de corta duraci\u00f3n y con un alcance limitado, Nois-IA representa un hito clave<\/strong> para la compa\u00f1\u00eda: es el primer proyecto donde integramos inteligencia artificial como eje central de desarrollo tecnol\u00f3gico para estudiar datos de cavitaci\u00f3n<\/strong> de campa\u00f1as ya realizadas, consolidando nuestra apuesta por la transformaci\u00f3n digital del sector de la ingenier\u00eda naval.<\/span><\/p>\n

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T<\/span>SI<\/span> takes a step forward in its innovation strategy with the Nois-IA project, a six-month pilot initiative that marks the beginning of our incursion into the development of solutions based on Artificial Intelligence (AI). This project, led by the R&D&I team, explores the application of intelligent algorithms for the detection and prediction of cavitation and Radiated Water Noise (URN) in different environments.<\/span><\/span><\/h2>\n

Although it is a short-term project with a limited scope, Nois-IA represents a key milestone for the company: it is the first project where we integrate artificial intelligence as the backbone of technological development to study cavitation data from campaigns already carried out, consolidating our commitment to the digital transformation of the engineering sector.<\/span><\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” min_height=”894.3px” custom_padding=”0px|||||” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.27.4″ _module_preset=”default” width=”78%” min_height=”834px” custom_padding=”30px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_divider color=”#64A70B” _builder_version=”4.23.2″ _module_preset=”default” width=”30%” max_width=”30%” module_alignment=”center” min_height=”32.8px” custom_margin=”||3px|||” global_colors_info=”{}”][\/et_pb_divider][et_pb_image src=”https:\/\/tsisl.es\/wp-content\/uploads\/2025\/04\/pexels-valeriya-13168711.jpg” title_text=”pexels-valeriya-13168711″ _builder_version=”4.27.4″ _module_preset=”default” width=”68%” module_alignment=”center” custom_margin=”|108px|-16px|||” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font=”Krub|300|||||||” text_text_color=”#041E42″ header_4_font=”Krub||||||||” header_4_text_color=”#041E42″ header_4_line_height=”1.7em” min_height=”634.5px” custom_margin=”35px|||-47px||” custom_padding=”|||48px||” inline_fonts=”Krub,Albert Sans” global_colors_info=”{}”]<\/p>\n

An Emerging Challenge: Cavitation and URN Prediction<\/span><\/strong><\/span><\/h3>\n

<\/strong><\/span><\/p>\n

Underwater noise pollution<\/strong> has become one of the most pressing environmental challenges in recent years. Institutions such as the European Union<\/strong> have acknowledged this issue due to its harmful impact on the marine environment<\/strong> as well as its implications for the maintenance and performance of vessels<\/strong>.<\/span><\/p>\n

In this context, the Nois-IA<\/strong> project emerges as an innovative response, leveraging Artificial Intelligence<\/strong> to anticipate and mitigate phenomena such as cavitation<\/strong> and Underwater Radiated Noise (URN)<\/strong>. The project aims to enhance both operational efficiency<\/strong> and the preservation of the underwater environment<\/strong>.<\/span><\/p>\n

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Real-World Validation and Technology Transfer<\/span><\/strong><\/h3>\n

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Nois-IA follows an experimental approach, developing a predictive model<\/strong> based on various AI algorithms<\/strong>, including neural networks<\/strong>. This model will be trained and validated using real data from ship measurement campaigns<\/strong>.<\/span><\/p>\n

The system will be capable of: Identifying acoustic behaviour patterns. Anticipating cavitation peaks. Providing actionable insights for decision-making in areas such as structural health<\/strong> and vessel mobility<\/strong>.<\/span><\/p>\n

<\/span><\/p>\n

A First Step Towards Quieter Seas<\/span><\/strong><\/h3>\n

<\/span><\/p>\n

With the support of the Digitalisation Department of the Community of Madrid<\/strong>, Nois-IA lays the groundwork for a new generation of AI-powered tools for monitoring, detecting, and predicting underwater noise<\/strong>. Its greatest value lies in its ability to: Open new lines of research. Drive the digital transformation of acoustics, sound, and vibration engineering<\/strong> within the maritime sector. Strengthen the connection between engineering, technology, and sustainability<\/strong>. This project not only enhances the technological capabilities of TSI<\/strong>, but also reinforces its commitment to responsible innovation and the future of our oceans.<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” background_color=”#041E42″ background_enable_image=”off” parallax=”on” background_blend=”darken” background_video_mp4=”https:\/\/tsisl.es\/wp-content\/uploads\/2025\/03\/18069701-uhd_3840_2160_24fps.mp4″ da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row column_structure=”1_4,1_4,1_4,1_4″ make_equal=”on” _builder_version=”4.24.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.24.2″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”3vh|3vh|3vh|3vh|true|true” global_colors_info=”{}”][et_pb_icon font_icon=”||divi||400″ icon_width=”50px” _builder_version=”4.25.2″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_icon][et_pb_text _builder_version=”4.25.2″ _module_preset=”default” text_font=”Arvo||||||||” text_text_color=”#041E42″ text_orientation=”center” global_colors_info=”{}”]<\/p>\n

Machine Learning<\/p>\n

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for database processing.<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.24.2″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”3vh|3vh|3vh|3vh|true|true” global_colors_info=”{}”][et_pb_icon font_icon=”6||divi||400″ icon_width=”50px” _builder_version=”4.25.2″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_icon][et_pb_text _builder_version=”4.25.2″ _module_preset=”default” text_font=”Arvo||||||||” text_text_color=”#041E42″ text_orientation=”center” global_colors_info=”{}”]<\/p>\n

Improving efficiency<\/p>\n

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of the ships<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.24.2″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”3vh|3vh|3vh|3vh|true|true” global_colors_info=”{}”][et_pb_icon font_icon=”T||divi||400″ icon_width=”50px” _builder_version=”4.25.2″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_icon][et_pb_text _builder_version=”4.25.2″ _module_preset=”default” text_font=”Arvo||||||||” text_text_color=”#041E42″ text_orientation=”center” global_colors_info=”{}”]<\/p>\n

Increased reliability<\/p>\n

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of ships’ systems.<\/p>\n

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Monitoring<\/h4>\n

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of structural health.<\/p>\n

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What are its Objectives?<\/p>\n

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For the prediction of noise levels, for example in highly sensitive areas.<\/p>\n

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With algorithms to anticipate propeller cavitation peaks without intensive manual processing.<\/p>\n

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and application of AI algorithms in the naval domain.<\/p>\n

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For future lines of research in R+D+i.<\/p>\n

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From early stages of naval design.<\/p>\n

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Specialised regulations on underwater noise.<\/p>\n

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A unique challenge for TSI<\/span><\/strong><\/h3>\n

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TSI brings its extensive experience in the study of noise generated by acoustic sources, especially propeller cavitation. It leads the development of a system based on AI – Machine Learning capable of estimating and predicting the acoustic behaviour of ships using real data from past campaigns.<\/p>\n

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  • The system is trained with cavitation data collected in previous campaigns, allowing to create a solid basis for the models.<\/li>\n
  • Preparation of URN models and estimation systems for the project demonstrators.<\/li>\n
  • Development of a predictive model of radiated noise to water (URN) from shipboard measurements.<\/li>\n
  • Application of AI algorithms on raw cavitation signals, saving time in processing and labelling.<\/li>\n
  • Creation of long-term reusable predictive models for future campaigns.<\/li>\n<\/ul>\n

    [\/et_pb_text][et_pb_image src=”https:\/\/tsisl.es\/wp-content\/uploads\/2025\/03\/Nois-IA-Project.jpg” alt=”Nois-IA Project” title_text=”Nois-IA Project” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”52px|||||” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”|auto|-10px|auto||” custom_padding=”15px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.25.2″ _module_preset=”default” background_color=”rgba(151,140,135,0.31)” text_orientation=”center” width=”100%” module_alignment=”center” custom_padding=”2vh|5vh|2vh|5vh|true|true” global_colors_info=”{}”]<\/p>\n

    Nois-IA is funded by the programmes to boost digitalisation of the Regional Ministry of Digitalisation of the Community of Madrid, a body that supports initiatives aimed at technological development and the application of new technologies in strategic sectors. It also has the support of the Ministry for Digital Transformation and the Civil Service, and the backing of the Recovery, Transformation and Resilience Plan financed by the European Union – NextGenerationEU Funds.<\/p>\n

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    PROJECT START DATE<\/p>\n

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    \u00a0<\/div>\n

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    [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

    01\/01\/2025<\/p>\n

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    PROJECT END DATE<\/p>\n

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    30\/06\/2025<\/p>\n

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    PROJECT REFERENCE<\/p>\n

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    ID 101192302<\/p>\n

    <\/p>\n

    \u00a0<\/p>\n

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    SUBSIDY AMOUNT<\/p>\n

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    26.741,41\u20ac<\/p>\n

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    PROJECT BENEFICIARY<\/p>\n

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    T\u00c9CNICAS Y SERVICIOS DE INGENIER\u00cdA S.L.<\/p>\n

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    Project Nois-IA: A pioneering step towards predicting ship noise using Artificial Intelligence. Explore how we are applying advanced data analysis to better understand cavitation noise and contribute to more efficient and sustainable shipping. Join us in this first foray into the use of AI for ship acoustic engineering.<\/p>\n","protected":false},"author":4,"featured_media":41281,"comment_status":"open","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"project_category":[82,81],"project_tag":[466,383,459,131,129,311],"class_list":["post-40738","project","type-project","status-publish","has-post-thumbnail","hentry","project_category-marine-proyects","project_category-rdi-europe-proyects","project_tag-cavitation","project_tag-innovative-technologies","project_tag-national-project","project_tag-rd","project_tag-tsi-en","project_tag-urn-en"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/project\/40738","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/project"}],"about":[{"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/types\/project"}],"author":[{"embeddable":true,"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/comments?post=40738"}],"version-history":[{"count":20,"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/project\/40738\/revisions"}],"predecessor-version":[{"id":43006,"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/project\/40738\/revisions\/43006"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/media\/41281"}],"wp:attachment":[{"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/media?parent=40738"}],"wp:term":[{"taxonomy":"project_category","embeddable":true,"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/project_category?post=40738"},{"taxonomy":"project_tag","embeddable":true,"href":"https:\/\/tsisl.es\/en\/wp-json\/wp\/v2\/project_tag?post=40738"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}