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Latest articles for Measurement Science and Technology

IOPscience

  • Miniaturization of optimal six-port reflectometer based on reduced-length coupled-line directional couplers
    Owing to their simple structure, six-port reflectometers (SPRs) are used as alternatives to vector network analyzers (VNAs) for reflection-coefficient sensing. However, the circuit areas of existing SPRs are generally too large for integration into microwave measurement systems, and most do not satisfy the optimal design criteria. In this paper, a miniaturized SPR with an optimal distribution of the circle center is proposed based on four directional couplers. The SPR comprises one codirectional coupler and three transdirectional couplers, and the coupling coefficients of the directional couplers are derived theoretically. The circuit area of the proposed SPR can be reduced by miniaturizing each directional coupler based on the tandem coupled lines. The proposed SPR was designed and fabricated at 2–3 GHz using strip lines. The measured S-parameters and circle center distributions were in good agreement with the theoretical derivations and electromagnetic simulations. Attenuators with different attenuation values were measured using the proposed SPR and a VNA, and the results showed good measurement consistency of the proposed SPR and VNA. Compared to other SPRs, the proposed SPR features the reliability of reflection coefficient measurements and flexibility in size.

  • Empowering smart measurement of digital infrastructures with self-sensing concrete
  • Fault diagnosis methods for electromechanical special equipment: review and prospects
    With the rapid advancement of industrial technology and the continuous expansion of production scale, electromechanical special equipment (ESE), such as cranes, has become increasingly essential in industrial operations. The demands for safety, reliability, and operational efficiency of this equipment have grown significantly. Extensive research has been conducted on fault diagnosis (FD) technologies for ESE to enhance their operational lifespan and ensure personnel safety. However, despite the considerable body of research, a comprehensive and systematic review in this field remains conspicuously absent. Therefore, this paper summarizes and analyzes the FD technologies associated with ESE, detailing the principal processes and the current status of technology applications. The typical failure modes that can compromise the performance are analyzed by thoroughly examining the core components of such equipment. Moreover, an in-depth discussion on FD methodologies is provided from the perspectives of traditional methods, signal processing methods, and machine learning methods. By examining these methods side by side, their strengths, limitations, and suitability for different types of ESE are highlighted. Finally, this paper offers a forward-looking assessment of the future development trajectory for FD technologies for ESE. This paper provides a comprehensive review and outlines a future development path on FD techniques in ESE, which will contribute to the continued development of these techniques.

  • Master–slave AUV cooperative navigation algorithm based on nonlinear error compensation of observation matrix
    The master–slave autonomous underwater vehicle (AUV) cooperative navigation (CN) system employs the extended Kalman filter (EKF) to fuse the low-precision estimated position of the slave AUV with the high-precision master–slave ranging data, thereby correcting the positioning error of the slave AUV and enhancing its navigation accuracy. However, the EKF-based CN algorithm shows significant positioning errors when the observation model suffers from substantial linearization errors. Therefore, this paper proposes a master–slave AUV CN algorithm based on observation matrix compensation to reduce the impact of the linearization error of the observation model on positioning accuracy and improve the slave-AUV positioning accuracy. Based on the EKF algorithm framework, this method calculates the partial derivative of the linearization error of the ranging equation to the position state vector of the slave-AUV to obtain the correction component of the observation matrix, compensating for the linearization error and effectively enhancing the positioning accuracy of the slave-AUV. The accuracy and applicability of the proposed method were rigorously verified using simulation and lake water field trials. Compared to the EKF CN algorithm, the positioning error of the proposed algorithm is significantly smaller.

  • The number and size identification of multi-damage for plate structures using a novel semi-data driven method
    A novel semi-data driven method is proposed for multi-damage identification on plate structures, primarily divided into damage number and damage size identification task. To further enhance identification accuracy with a limited number of features and to select the optimal feature set, a novel method termed improved random forest based on recursive features elimination (RF-RFE) is integrated into the damage number identification process. After the optimal feature set is obtained, a classifier model is trained to identify the number of multi-damages. Following this, a one-to-one mapping relationship between the Mahalanobis distance and the minimal size of multi-damage is established and it is utilized to predict the minimal size of the multi-damage. To validate the proposed method, some experiments are conducted on aluminum plates and carbon fiber reinforced polymer (CFRP) plate with varying number and size of damage. The experimental results indicate that the proposed method effectively selects the optimal feature set from the original feature set, achieving identification accuracies of 99.17% and 97.71% with two and three features, respectively, in damage number identification tasks for aluminum plates and CFRP plates. Compared to other three commonly feature selection methods, the same classifier model obtains the largest value of four different evaluation metrics only by using the optimal feature set selected by our proposed improved RF-RFE. During the damage size identification process, a linear relationship between the minimal damage size of multi-damage and the Mahalanobis distance is established and it is employed to accurately predict the minimal damage size of multi-damage with a relative error 1.90%–8.53% in the aluminum plates and 0.67%–7.00% in the CFRP plates. These results jointly indicate that the proposed method can effectively and accurately identify both the number and size of multi-damage, and it provides a new approach for multi-damage quantification on plate structures.