Fraunhofer IPMS Revolutionizes Predictive Maintenance with Sensor Technology and AI

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Fraunhofer IPMS Revolutionizes Predictive Maintenance with Sensor Technology and AI

Fraunhofer IPMS unveils an innovative demonstrator for predictive maintenance, employing cutting-edge sensor technology and AI-based data processing. This advancement, stemming from insights gathered in the iCampus project ForTune, merges sensor integration, data acquisition, and AI analysis to revolutionize plant and machine maintenance practices.

Dr. Marcel Jongmanns, project leader at Fraunhofer IPMS, underscores the solution’s ability to enable specific condition monitoring, facilitating early damage detection and optimized maintenance scheduling to minimize downtime.

The demonstrator showcases a miniaturized conveyor belt, integrating multimodal sensors to detect geographic directions, rotation rates, electromagnetic fields, and sound or ultrasonic signals. AI models, derived from thorough data analysis, provide real-time predictive insights, enhancing maintenance efficacy.

Fraunhofer IPMS’s system integrates inside-the-home sensors with an edge computing unit, enabling real-time data processing and sophisticated AI operations. This breakthrough addresses hardware limitations, allowing seamless integration of numerous sensors for accurate predictive analysis. Collaborations with industry leaders like Vetter Kleinförderbänder underscore the sector’s keen interest in such solutions.

Attendees of the SENSOR+TEST trade fair in Nuremberg from June 11 to 13, 2024, can explore the demonstrator at Fraunhofer IPMS’s booth, engaging with experts for insights and discussions. Pre-scheduled appointments for in-depth discussions further highlight Fraunhofer IPMS’s commitment to advancing predictive maintenance technologies.

Predictive Maintenance: Fraunhofer’s Sensor Tech Innovation

A demonstrator for predicted maintenance of professional equipment is provided by the Fraunhofer Institute for Photonic Microsystems IPMS. The demonstrator uses cutting-edge sensor technology and AI-based data processing to spot potential machine damage early on and save time and money by using innovative sensor technology.

In response to the findings of the iCampus project ForTune, Fraunhofer IPMS has created a new demonstrator that combines sensor technology, data acquisition, and AI-based data evaluation for condition monitoring and forecast maintenance. This opens up new avenues for plant and machine preventative maintenance. Fraunhofer IPMS makes use of its expertise in real-time data transmission and edge computing. Dr. Marcel Jongmanns, project leader at Fraunhofer IPMS, explains:” Our solution enables specific condition monitoring of machines through the use of sensors and intelligent data analysis. By integrating AI into the sensors, we can detect damage before it occurs, optimise maintenance intervals and minimise downtime.

Predictive Maintenance Toolbox: Fraunhofer’s Innovative Sensor Integration

The exhibit features a miniaturized conveyor belt and illustrates the effectiveness of a novel toolbox for monitoring commercial plants. Multimodal sensors are used by the demonstration. The device function records both the geographic directions and the associated rotation rates. Additionally, professional equipment is monitored by electromagnetic field sensors and sound or ultrasonic sensors. The system provides two major functions: Belt tension detection and jam detection. The AI models are based on thorough analysis of data, which enables appropriate damage prediction. Real-time calibrations can be performed to adjust the system to new environments in order to increase the accuracy of the models.

The Fraunhofer IPMS system solution aims to integrate the inside-the-home sensors with its unique edge computing unit based on RISCV architecture to enable effective data processing right at the point of use. This enables sophisticated Artificial operations and real-time analysis. Changing economic influences can be instantly modeled or taken into account in the analysis. This enables the integration of a large number of sensors, which drastically improves the accuracy of predictions regarding the state of industrial equipment. Existing hardware limitations for real-time modeling in integrated systems are overcome. The advancement of the technology is supported by Fraunhofer IPMS’s expertise in the fields of sensor technology and Iot evaluation. Existing partnerships with companies such as Vetter Kleinförderbänder demonstrate the industry’s interest in for solutions.

New Edge AI solutions for intelligent sensors to help predict and maintain  industrial systems - Fraunhofer IPMS develops an intelligent demonstrator  for the predictive maintenance of industrial plants

Predictive Maintenance Showcase: Fraunhofer IPMS Booth at SENSOR+TEST Fair

During the SENSOR+TEST trade fair from June 11 to 13, 2024 in Nuremberg, visitors will have the opportunity to view the demonstrator at the Fraunhofer IPMS booth 1- 317. The scientists will be present to answer inquiries and provide insights into the research. On the Fraunhofer IPMS website, appointments for individual discussions can be made in advance.

Matthew Boyle

Matthew Boyle is a distinguished Smart City Consultant, renowned for his expertise in IoT (Internet of Things) and cutting-edge urban technology solutions. With a deep understanding of Smart City initiatives, Matthew excels in leveraging IoT innovations to transform urban landscapes into efficient, sustainable, and connected environments. His strategic insights and hands-on experience in urban planning, data analytics, and IoT implementation make him a trusted expert in the field. Matthew Boyle is your go-to consultant for navigating the complex world of Smart Cities, ensuring seamless integration of IoT technologies, and unlocking the potential of data-driven urban solutions. With his guidance, your city can thrive in the digital age, enhancing quality of life and fostering a sustainable future.

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