Virtual Sensors for Smart Data Generation and Processing in AI-Driven Industrial Applications

Etxegarai, Maddi; Camps, Marta; Echeverria, Lluís; Ribalta, Marc; Bonada, Francesc; Domingo, Xavier

doi:10.5772/intechopen.106988

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Hu et al extrapolated the force exerted at the catheter tip within the human body with a mathematical model, exploiting a sensor positioned on the exterior of the catheter tube [6]. In recent years, with the advent of new technologies that provide enhanced data storage capabilities, data-driven methods have gained significant attention thanks to the development of new machine learning algorithms [7,8]. This innovative data-driven approach offers a new, effective means of obtaining accurate readings along the entire processing chain, without installing expensive hardware, providing an effective tool for the online estimation of quantities that are important but difficult to measure with traditional sensors.…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Virtual Sensors for Real-Time Dynamic Analysis of Mechanisms: A Feasibility Study

Fabiocchi,

Giulietti,

Carnevale

et al. 2024

Machines

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The measurement of the ground forces on a real structure or mechanism in operation can be time-consuming and expensive, particularly when production cannot be halted to install sensors. In cases in which disassembling the parts of the system to accommodate sensor installation is neither feasible nor desirable, observing the structure or mechanism in operation and quickly deducing its force trends would facilitate monitoring activities in industrial processes. This opportunity is gradually becoming a reality thanks to the coupling of artificial intelligence (AI) with design techniques such as the finite element and multi-body methods. Properly trained inferential models could make it possible to study the dynamic behavior of real systems and mechanisms in operation simply by observing them in real time through a camera, and they could become valuable tools for investigation during the operation of machinery and devices without the use of additional sensors, which are difficult to use and install. In this paper, the idea presented is developed and applied to a simple mechanism for which the reaction forces during operating conditions are to be determined. This paper explores the implementation of an innovative vision-based virtual sensor that, through data-driven training, is able to emulate traditional sensing solutions for the estimation of reaction forces. The virtual sensor and relative inferential model is validated in a scenario as close to the real world as possible, taking into account interfering inputs that add to the measurement uncertainty, as in a real-world measurement scenario. The results indicate that the proposed model has great robustness and accuracy, as evidenced by the low RMSE values in predicting the reaction forces. This demonstrates the model’s effectiveness in reproducing real-world scenarios, highlighting its potential in the real-time estimation of ground reaction forces in industrial settings. The success of this vision-based virtual sensor model opens new avenues for more robust, accurate, and cost-effective solutions for force estimation, addressing the challenges of uncertainty and the limitations of physical sensor deployment.

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Section: Introductionmentioning

confidence: 99%

AI-Driven Virtual Sensors for Real-Time Dynamic Analysis of Mechanisms: A Feasibility Study

Fabiocchi,

Giulietti,

Carnevale

et al. 2024

Machines

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Overcoming Challenges in Traditional Waste Water Treatment Through AI-Driven Innovation

Sakkaravarthy,

Jano,

Vijayakumar

2024

Springer Water

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Recurrent neural networks as virtual cavity pressure and temperature sensors in high-pressure die casting

Rudack,

Rom,

Bruckmeier

et al. 2024

Int J Adv Manuf Technol

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High-pressure die casting (HPDC) is a permanent mold-based production technology that facilitates the casting of near net shape components from nonferrous alloys. The pressure and temperature conditions within the cavity impact the cast product quality during and after the conclusion of the die filling process. Die surface cavity sensors can deliver information describing the conditions at the die-casting interface. They are associated with high costs and limited service lifetimes below the achievable total cycle count of the die inserts and therefore ill-suited for industrial use cases. In this work, the suitability of long short-term memory (LSTM) recurrent neural networks (RNN) for substituting physical cavity temperature and pressure sensors virtually after the production ramp-up or at the end of the sensor service life is investigated. Training LSTMs with data of 233 casting cycles with different process parameters provides networks which are then applied to 99 further cycles. The prediction accuracy is investigated for different time interval lengths in the solidification and cooling phase. For longer time intervals, the cavity pressure prediction deteriorates, potentially due to a highly individual and hardly ascertainable buildup of casting distortion and internal stresses. Overall, however, the accuracy of the developed LSTMs is excellent for the cavity temperatures and good for the cavity pressures.

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Virtual Sensors for Smart Data Generation and Processing in AI-Driven Industrial Applications

Cited by 5 publications

References 26 publications

AI-Driven Virtual Sensors for Real-Time Dynamic Analysis of Mechanisms: A Feasibility Study

AI-Driven Virtual Sensors for Real-Time Dynamic Analysis of Mechanisms: A Feasibility Study

Overcoming Challenges in Traditional Waste Water Treatment Through AI-Driven Innovation

Recurrent neural networks as virtual cavity pressure and temperature sensors in high-pressure die casting

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