Digital Twin and Machine Learning for Enhanced Safety and Functionality
Detectium revolutionizes anomaly detection and fire prevention for industrial facilities by integrating electro-optical/infrared (EO/IR) sensors technology with digital twinning and machine learning algorithms.
The Solution
Our solution combines real-time 3D visualization with advanced sensors, data analytics, and machine learning to create a digital twin for companies. This enables early detection of fires and allows the use of historical fire data to mitigate the risk of fire.
The first system is piloted at a partner’s facility which assists with the development of the sensors, 3D visualization, and data analytics system integration.
The first system is piloted at a partner’s facility which assists with the development of the sensors, 3D visualization, and data analytics system integration.
Product and Service Offerings
B2B - Industrial Uses
A digital twin is a virtual representation of physical objects in real-time. Our solution consists of hardware and software components, including UV and Infrared sensors, 3D model visualization, and analytics based on collected data to improve fire safety. We can detect fire much faster than the existing solutions. We are providing a digital twin of factories to assist with fire damage risk mitigation.
Our highly sensitive UV sensors can detect even small flames indoors and outdoors. The operation of our flame detector is inherently much faster than conventional fire detectors that are based on the detection of smoke or heat. The first prototype of our UV detector (patent pending) had been developed in collaboration with CERN.
Depending on the complexity and size of the site, we offer a flexible 3D visualization service for industrial customers to be utilized for various applications including workflow simulation and optimization.
Based on historical data and prediction, we provide data analysis to foresee the fire hazard risks at your facility. You can subscribe to our software as a service for a historical fire data analytics dashboard.
B2C - Household Uses
For non-industrial customers, a simplified version of our solution is offered which can be used to enhance security in addition to fire safety.
The Benefits of Detectium's Solution
Speed
Our solution uses a highly sensitive IR detector capable of detecting small temperature anomalies as well as flames indoors and outdoors. This allows us to detect fire and notify the fire distinguishing system quicker than the current fire detection system.
Accuracy
The range of our sensors is large. Our solution can detect a flame from 60 meters away. The range of current sensors used for fire detection is about 10.5 meters for smoke detectors, while the range for the heat detectors is about 7.5 meters.
Use of data
Our solution leverages machine learning algorithms to analyze the historical data collected at the factory over an extended period, pertaining to various fire incidents. By doing so, it identifies and maps high-risk areas for more effective future risk mitigation strategies.
The Benefits of Digital Twin for Safety
Team
Siavash Khajavi
Siavash is the CEO and co-founder of Detectium. He received a doctorial degree in Industrial Engineering and Management from Aalto University in 2018. He has conducted extensive research on digital twins. His vision is to create a world where the digital representation of all objects and entities can be efficiently and accurately created to enhance efficiency and safety in the physical world.
Alberto Xamin
Alberto is a technical developer at Detectium, specializing in 3D modeling and design. He holds a master’s degree in Robotics and Artificial Intelligence from KTH and Aalto University. Alberto is responsible for 3D visualization and the technical aspects of the project. He has worked at companies such as Meta and Datadog, bringing valuable experience to the team.
Adriaan Knapen
Adriaan is a technical developer at Detectium. His wide range of expertise includes embedded software development, release and test automation, cloud infrastructure, and software development culture.
Advisors
Arto Niemela
Dr. Arto Niemela is a well-regarded scientist in the field of sensor development in Finland. Fenno-Aurum Oy Ltd designs and manufactures radiation detectors for X-ray, gamma-ray, and Ultra Violet detection applications. He is a technical advisor of this project.
Andrea Chiancone
Dr. Andrea Chianocone is the person responsible for this project at TeamDev. TeamDev Company designs and develops software solutions to support manufacturing and service organizations and public administrations to deal with digital transformation processes effectively. He is an advisor on business and data analytics.
Company Background
Established in 2021, Detectium is a startup in Finland. The project results from a collaboration between three partners: Fenno-Aurum, TeamDev, and Aalto University, to reduce casualties from industrial fires by digital twinning technology and hi-tech sensors.
Fenno-Aurum is our launch customer and hardware supplier providing their established hardware experience and sensor innovations to continuously collect accurate data about the factory environment.
TeamDev is our project business champion who provides their expertise in machine learning, which is used to analyze the sensor data in real-me and yields actionable insights about improving the factory’s safety.
Aalto University carries over their proficiency in building simulation models from previous projects, which will manifest themselves as a digital twin of the factory. This digital twin will source its data from Fenno-Aurum’s sensors and TeamDev’s data analytics, integrating both into a virtual representation of the state of factory safety.
This project has received seed funding from the EIT Digital 2021 program as an innovation activity in the area of the digital industry under grant number 20644 (Project ID: 21325).
Digital twin definition and background:
A digital twin is the digital counterpart of a physical asset that specifically works with real-time data fed by sensor systems to record and analyze the real-time structural and environmental parameters of a physical asset. This is done for the purpose of performing highly accurate simulations and data analytics.
Partners
Contact
