A novel torque sensor based on the angle of magnetization vector

Huang, Yinguo; Yang, Yong; Zhang, Xiaomei; Zhao, Meirong

doi:10.1186/s13638-018-1247-6

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…This work offered a viable technique to support condition-based maintenance and early fault detection systems by tracking component vibrations. Later, Huang et al [349] reported an online torque monitoring approach relying on advanced ML models to harvest magnetization vector deflection angle and different torsional loads. The authors recorded a maximum torque sensitivity of 17.7 mV/Nm and a nonlinearity error of 0.77% FS in a range from 9.71 Nm to 93.35 Nm.…”

Section: ) Force and Torque Sensorsmentioning

confidence: 99%

Strain Sensing Technology to Enable Next-Generation Industry and Smart Machines for the Factories of the Future: A Review

Hamed,

O’Donnell,

Lishchenko

et al. 2023

IEEE Sensors J.

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In the era of digitalization, there is a huge focus on capturing data from manufacturing processes and systems. Since the promotion of Industry 4.0, the industrial marketplace has been crowded with solution providers who excel in capturing and aggregating data on the cloud and presenting it on dashboards. From machine states to production targets, this type of data has become more readily available from tools, controllers, switches, and factory bus systems. As the industry pushes deeper into the machine for information and aspires to have smart machines that can feel and react to experiences during the manufacturing process, then more sophisticated technology is necessary. Strain sensor technology is a logical measurement principle to address this challenge for many industrial sectors. For researchers and industrialists to progress toward the smart machine agenda, there must be a firm grasp of the "technology-solution fit," i.e., what strain technology could provide the most appropriate solution. This paper presents a review of the state of the art in strain sensors that are foreseen as frontrunners to enable next-generation smart components and intelligent tools. The review focuses on industrial strain sensing technologies that are at a mature place on the technology readiness levels (TRLs) and present themselves as highly practical solutions for smart components and digitized machines, considering sensitivity, powered or passive, wired or wireless, and robustness. Through this review, researchers and industrialists will have a suite of solutions to move on with their innovative designs of smart machines based on embedded strain sensor technology.

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Section: ) Force and Torque Sensorsmentioning

confidence: 99%

Strain Sensing Technology to Enable Next-Generation Industry and Smart Machines for the Factories of the Future: A Review

Hamed,

O’Donnell,

Lishchenko

et al. 2023

IEEE Sensors J.

View full text Add to dashboard Cite

show abstract

“…Despite its simple installation process, this method exhibits limitations in measurement precision, susceptibility to external interferences, and relatively slow response speed. Apart from this method, in [ 12 ], the relationship between the deflection angle of the magnetization vector and torque is established based on the principle of electromagnetic effects and a non-contact torque measurement system utilizing Hall sensors is proposed. In [ 13 ], a torque measurement device based on the Hall effect is also introduced.…”

Section: Introductionmentioning

confidence: 99%

Optoelectronic Torque Measurement System Based on SAPSO-RBF Algorithm

Xia,

Lou,

Yuan

et al. 2024

Sensors

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The torque is a significant indicator reflecting the comprehensive operational characteristics of a power system. Thus, accurate torque measurement plays a pivotal role in ensuring the safety and stability of the system. However, conventional torque measurement systems predominantly rely on strain gauges adhered to the shaft, often leading to reduced accuracy, poor repeatability, and non-traceability due to the influence of strain gauge adhesion. To tackle the challenge, this paper introduces a photoelectric torque measurement system. Quadrants of photoelectric sensors are employed to capture minute deformations induced by torque on the rotational axis, converting them into measurable voltage. Subsequently, the system employs the radial basis function neural network optimized by simulated annealing combined with particle swarm algorithm (SAPSO-RBF) to establish a correlation between measured torque values and standard references, thereby calibrating the measured values. Experimental results affirm the system’s capability to accurately determine torque measurements and execute calibration, minimizing measurement errors to 0.92%.

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A precision-drive hysteresis model with an equal-density weight function for GMA feedforward compensation

Kun

Wang

et al. 2023

Nanotechnology and Precision Engineering

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Giant magnetostrictive actuators (GMAs) are a widely used type of micro-nano actuator, and they are greatly significant in the field of precision engineering. The accuracy of a GMA often depends on its hysteresis model. However, existing models have some limitations, including the difficulty of identifying their parameters and the tradeoff between the quantity of modeling data required and the level of precision achieved. To solve these problems, in this paper, we propose a Preisach inverse model based on equal-density segmentation of the weight function (E-Preisach). The weight function used to calculate the displacement is first discretized. Then, to obtain a finer weight distribution, the discretized geometric units are uniformly divided by area. This can further minimize the output displacement span, and it produces a higher-precision hysteresis model. The process of parameter identification is made easier by this approach, which also resolves the difficulty of obtaining high precision using a small amount of modeling data. The Preisach and the E-Preisach inverse models were investigated and compared using experiments. At frequencies of 1 and 5 Hz, it was found that the E-Preisach inverse model decreases the maximum error of the feedforward compensation open-loop control to within 1 µm and decreases the root-mean-square error in displacement to within 0.5 µm without the need to increase the number of measured hysteresis loops. As a result, the E-Preisach inverse model streamlines the structure of the model and requires fewer parameters for modeling. This provides a high-precision modeling method using a small amount of modeling data; it will have applications in precision engineering fields such as active vibration damping and ultra-precision machining.

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A novel torque sensor based on the angle of magnetization vector

Cited by 7 publications

References 18 publications

Strain Sensing Technology to Enable Next-Generation Industry and Smart Machines for the Factories of the Future: A Review

Strain Sensing Technology to Enable Next-Generation Industry and Smart Machines for the Factories of the Future: A Review

Optoelectronic Torque Measurement System Based on SAPSO-RBF Algorithm

A precision-drive hysteresis model with an equal-density weight function for GMA feedforward compensation

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