Design and Validation of a Miniature Fiber Bragg Grating-Enabled High-Sensitivity Torque Sensor

Lai, Dewei; Tang, Zhongxin; Zhao, Jianchang; Wang, Shuxin; Shi, Chaoyang

doi:10.1109/jsen.2021.3095275

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…3). Similarly, Lai et al [187] reported a cylindrical hexaform structure supported by adjustable hinges and integrated with an FBG-based torque sensor to obtain high sensitivity and a small footprint. The authors proposed a suitable design for multi-finger robots and minimally invasive surgical (MIS) instruments.…”

Section: ) Force and Torque Sensorsmentioning

confidence: 99%

“…They built a system that can identify smaller leakage amounts with improved localization precision for condition-based maintenance and minimal false alarms. In [187] and [188], the authors worked on developing a novel hoop strain FBG sensor to mimic the pipeline's internal pressure variations and wall thickness degradation. This prototype can enable simultaneous monitoring of processes in water, oil, and gas pipelines.…”

Section: ) Pressure Sensors and Flowmetersmentioning

confidence: 99%

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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%

Section: ) Pressure Sensors and Flowmetersmentioning

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

“…Subsequently, a sliding window algorithm is employed to reduce measurement errors and improve accuracy. In [ 18 ], a high-sensitivity, compact torque sensor based on Fiber Bragg Grating (FBG) technology is also presented. This sensor comprises a torque-sensitive bending structure and two diagonally arranged optical fibers, incorporating an embedded fiber Bragg grating sensor.…”

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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