• Chinese Journal of Lasers
  • Vol. 48, Issue 16, 1610004 (2021)
Yijia Li, Zhengfang Wang, Jing Wang*, and Qingmei Sui
Author Affiliations
  • School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
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    DOI: 10.3788/CJL202148.1610004 Cite this Article Set citation alerts
    Yijia Li, Zhengfang Wang, Jing Wang, Qingmei Sui. Damage Identification of I-Beam Based on Fiber Bragg Grating Vibration Sensor and Extreme Learning Machine[J]. Chinese Journal of Lasers, 2021, 48(16): 1610004 Copy Citation Text show less

    Abstract

    Objective The potential damages of an I-beam structure may lead to safety accidents and adversely affect the safe operation of large-scale infrastructures such as buildings and bridges. The structural state monitoring and damage identification of an I-beam are critical to ensure its safe operation. Compared with traditional electronic-based vibration monitoring techniques, the fiber Bragg grating (FBG) sensor is small, high in sensitivity, anti-electromagnetic interference, and easy multiplexing. It is thus preferred to be employed for collecting the vibration signal of the I-beam structure of the infrastructure. However, the existing FBG vibration sensors are difficult to meet the requirements of wide frequency response and high sensitivity measurement of I-beam. Therefore, the first issue to be solved is the accurate sensing techniques of vibration signals. Apart from vibration sensing, another issue to be tackled lies in the damage identification of I-beam structure. The neural network and its transformer-based identification methods that have been developed recently depend on a large amount of training data and are slow in training, which limit its generalization in practical applications. To solve the aforementioned two issues, a novel FBG vibration sensor with two ends fixed beam was designed for vibration sensing. Moreover, the extreme learning machine (ELM) algorithm was employed to identify the damage state of the I-beam. We hope the results can be helpful to promote the safety monitoring and damage evaluation techniques of I-beam structures.

    Methods In this paper, a novel FBG vibration sensor with a double-ended fixed beam structure was designed, and the ELM-based automatic damage identification method of the I-beam was studied. First, a novel I-shape beam structure was designed as vibration-sensitive components of the FBG sensor. Then, we performed finite element simulations as well as testing experiments on the sensor to test its performance. After that, the energy ratio variation deviation (ERVD) of the wavelet packet was extracted as the damage feature to characterize the damage state of the I-beam. In addition, then the ELM algorithm was followed for the damage evaluation of the I-beam, The back propagation (BP) based damage identification method was used as a baseline model for comparison.

    Results and Discussions In this paper, a novel FBG sensor with a beam structure fixed at both ends was developed, and the ELM classification method was used to realize the damage identification of the beam structure. The results of the simulation and experiments show that the novel FBG vibration sensor has good acceleration response and time-frequency response characteristics within the acceleration range of 0.2g--3.0g and the operating frequency range of 10--350 Hz. The natural frequency of the sensor, when the acceleration was 2.0g was about 543.9 Hz (Fig. 7), agreed well with the simulation result of 568.6 Hz. The sensitivity of the sensor was approximately 6.7 pm/g, and the repeatability error of the sensor was about 1.7% (Fig. 9). The lateral sensitivity was 4.2% of the longitudinal sensitivity (Fig. 10). In addition, the structural damage index, ERVD, was about 3.2 for Case 1), and it significantly increased to about 15.7 for Case 2). For Case 3), the ERVD increased to about 23.0, which indicated that it was capable of reflecting the damage state of the beam structure (Fig. 15). After being tested on 90 groups of test samples under three working conditions, the accuracy rates of damage identification of the ELM and BP-based damage evaluation models were 96.7% and 93.3%, respectively (Table 2 and Fig. 16).

    Conclusions Focusing on the requirements of reliable condition monitoring and effective damage identification of I-beam, this paper developed a novel FBG vibration sensor with a fixed beam structure at both ends and then proposed the ELM classification method to realize the damage identification of the beam structure. The results of the simulation and performance test of the sensor show that the sensor has good acceleration response characteristics in the range of acceleration 0.2g--3.0g and operating frequency 10--350 Hz and has a good repeatability and a lateral anti-interference ability. It was applied to the beam structural damage identification process for inspection, using the ERVD as the damage feature, and the ELM was used to identify the beam structure damage. The preliminary test shows that its learning speed is fast, the generalization ability is good, and the damage degree identification accuracy reaches 96.7%, which is 3.4 percent higher than BP damage identification method.

    Yijia Li, Zhengfang Wang, Jing Wang, Qingmei Sui. Damage Identification of I-Beam Based on Fiber Bragg Grating Vibration Sensor and Extreme Learning Machine[J]. Chinese Journal of Lasers, 2021, 48(16): 1610004
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