A Steel Wire Stress Measuring Sensor Based on the Static Magnetization by Permanent Magnets

Deng, Dongge; Wu, Xiaowen; Zuo, Shifan

doi:10.3390/s16101650

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…Zhang et al [7] designed a new type of acoustic filtering sensor, with a cylindrical cavity and a narrow gap which can greatly absorb vibration energy to realize accurate wire rope tension measurement. Deng et al [8] proposed a new stress measuring sensor including a static magnetization unit made of permanent magnets and a magnetic field measurement unit to evaluate the axial stress in steel wires. Xu et al [9] designed a wireless sensor network technology adapted to underground channel conditions, which has important theoretical and practical value for safety monitoring in underground coal mines.…”

Section: Introductionmentioning

confidence: 99%

Study on Dynamic Monitoring of Wire Rope Tension Based on the Particle Damping Sensor

Lei

Gao

Zhang

et al. 2019

Sensors

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Real-time monitoring of wire rope tension is of great significance to the safe operation of mine hoist. Due to the longitudinal and lateral coupling vibration of wire ropes during the operation of hoist, there are high frequency components in measured tension signals of wire ropes, which cannot effectively characterize the actual lifting load. To overcome this problem, a particle damping sensor with a vibration dissipation function is designed in this paper. Multilayered steel balls are placed into the cylindrical cavity of the sensor. Damping vibration and energy dissipation will occur when the sensor is subjected to external excitation. Then, to obtain the optimal sensor characteristics, relevant parameters of the particles and the spoke structure are simulated. Finally, the sensor based on the optimized parameters is manufactured and tested in a coal mine. Compared with the general pressure sensor, the particle damping sensor can effectively eliminate the influence of wire ropes vibration on tension measurement and achieve accurate measurement results.

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Section: Introductionmentioning

confidence: 99%

Study on Dynamic Monitoring of Wire Rope Tension Based on the Particle Damping Sensor

Lei

Gao

Zhang

et al. 2019

Sensors

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show abstract

“…13 In addition, a permanent magnet-based stress sensor measurement was attempted using static magnetization. 14 Machine learning techniques have been applied to the structural health monitoring (SHM) of infrastructures because of their excellent pattern recognition or classification. [15][16][17][18][19] An experimental study was performed on a three-story aluminum frame structure using a load cell and four accelerometers with four machine learning techniques: auto-associative neural network (AANN), factor analysis (FA), singular value decomposition (SVD), and Mahalanobis squared distance (MSD).…”

Section: Introductionmentioning

confidence: 99%

“…13 In addition, a permanent magnet-based stress sensor measurement was attempted using static magnetization. 14…”

Section: Introductionmentioning

confidence: 99%

Field applicability of a machine learning–based tensile force estimation for pre-stressed concrete bridges using an embedded elasto-magnetic sensor

Kim

Park

2019

Structural Health Monitoring

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It has been proposed that pre-stressed concrete bridges improve load performance by inducing axial pre-stress using pre-stress tendons. However, the tensile force of the pre-stress tendons could not be managed after construction, although it directly supports the load of the structure. Thus, the tensile force of the pre-stress tendon should be checked for structural health monitoring of pre-stressed concrete bridges. In this study, a machine learning–based tensile force estimation method for a pre-stressed concrete girder is proposed using an embedded elasto-magnetic sensor and machine learning method. The feedforward neural network and radial basis function network were applied to estimate the tensile force of the pre-stress tendon using the area ratio of the magnetic hysteresis curve measured by the embedded elasto-magnetic sensor. The feedforward neural network and radial basis function network were trained using 213 datasets obtained in laboratory experiments, and trained feedforward neural network and radial basis function network were applied to a 50-m real-scale pre-stressed concrete girder test for estimating tensile force. Nine embedded elasto-magnetic sensors were installed on the sheath, and the magnetic hysteresis curves of the pre-stress tendons were measured during tensioning. The area ratio was extracted and inputted to the trained feedforward neural network and radial basis function network to estimate the tensile force. The estimated tensile force was compared with the reference tensile force measured by the load cell. According to the result, the estimated tensile force can represent the actual tensile force of the pre-stress tendon without calibrating tensile force estimation algorithms at the site. In addition, it can measure the actual friction loss by estimating the tensile force at the maximum eccentric part. Based on the results, the proposed method might be a solution for the structural health monitoring of pre-stressed concrete bridges with field applicability.

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Feasibility study of determining axial stress in ferromagnetic bars using reciprocal amplitude of initial differential susceptibility obtained from static magnetization by permanent magnets

Deng

2018

Journal of Magnetism and Magnetic Materials

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A Steel Wire Stress Measuring Sensor Based on the Static Magnetization by Permanent Magnets

Cited by 9 publications

References 30 publications

Study on Dynamic Monitoring of Wire Rope Tension Based on the Particle Damping Sensor

Study on Dynamic Monitoring of Wire Rope Tension Based on the Particle Damping Sensor

Field applicability of a machine learning–based tensile force estimation for pre-stressed concrete bridges using an embedded elasto-magnetic sensor

Feasibility study of determining axial stress in ferromagnetic bars using reciprocal amplitude of initial differential susceptibility obtained from static magnetization by permanent magnets

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