Abstract
Techniques based on the elasto-magnetic (EM) effect have been receiving increasing attention for their significant advantages in cable stress/force monitoring of in-service structures. Variations in ambient temperature affect the magnetic behaviors of steel components, causing errors in the sensor and measurement system results. Therefore, temperature compensation is essential. In this paper, the effect of temperature on the force monitoring of steel cables using smart elasto-magneto-electric (EME) sensors was investigated experimentally. A back propagation (BP) neural network method is proposed to obtain a direct readout of the applied force in the engineering environment, involving less computational complexity. On the basis of the data measured in the experiment, an improved BP neural network model was established. The test result shows that, over a temperature range of approximately −10 °C to 60 °C, the maximum relative error in the force measurement is within ±0.9%. A polynomial fitting method was also implemented for comparison. It is concluded that the method based on a BP neural network can be more reliable, effective and robust, and can be extended to temperature compensation of other similar sensors.
Highlights
Monitoring of cable stress/force of in-service structures is challenging but crucial to the evaluation of structural safety [1,2]
By using the above optimal parameters of the back propagation (BP) neural network, the final compensation results were obtained through training
The performance of EME sensors in cable force monitoring is greatly affected by temperature variations and relevant environmental conditions
Summary
Monitoring of cable stress/force of in-service structures is challenging but crucial to the evaluation of structural safety [1,2]. Uncertain factors that are impossible to predict cause the nonlinearity to vary from instrument to instrument, place to place and time to time Among these factors, temperature has a major impact on the EME sensor’s performance, which is the focus of this paper. Polynomial fitting is one commonly used software compensation method among traditional techniques [3] It uses a polynomial of degree n to approximate a nonlinear curve whose polynomial coefficients can be calculated using experimental data. As a novel information processing method, a BP neural network can be used for temperature compensation of EME sensors owing to their advantages of strong nonlinear mapping ability, parallel processing, error tolerance, adaptive ability, and self-learning capability [12,13,14].
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