Measurement最新文献

Partial discharge (PD) is a crucial cause of high voltage (HV) cable failures. Detecting and locating PD efficiently and effectively is paramount for HV cable maintenance. However, the attenuation of the PD signal after long-distance propagation makes it challenging to identify. This paper presents a distributed PD detection method that significantly reduces the propagation distance of PD signals before the sensor captures them. The technique uses the time-of-arrival to locate PD, whose localization accuracy is directly linked to the clock synchronization (CS) accuracy. CS between different sensors is achieved through three times interactions of the CS signal. The linear frequency modulation (LFM) signal is selected as the CS signal, which can generate a significant gain after pulse compression. It ensures that the CS signal can be effectively recognized after the attenuation of long-distance propagation. The paper also introduces a signal discrimination method based on the short-time Fourier transform that eliminates the influence of the LFM signal on PD identification. The cableless CS method makes it easier to set up before the onsite test. A prototype was implemented and tested. The CS accuracy was measured within 4 ns in the laboratory. An onsite test was carried out to evaluate the performance of the distributed PD detection system. A defect was found at a joint location, which was also confirmed by an additional ultrasonic PD test.

Pipe structures are commonly employed in modern industries. However, the transient pressure induced by water hammer leads to a high risk of structural damage, highlighting the importance of Structural Health Monitoring (SHM). The inverse Finite Element Method (iFEM) offers a way to reverse structural deformations based on a set of discrete strain data. In this paper, the water hammer phenomenon in a Reservoir-Pipe-Valve (RPV) system is investigated. The element iQS4 is adopted to reconstruct dynamic deformations for the pipe using iFEM. The water hammer resulting from an instantaneous closure of the valve can generate significant fluid pressure, leading to severe pipe vibrations. The vibration deformations can be monitored by iFEM. The maximum error of the pipe nodal displacement is below 3 % and the average error is below 2 % in the proposed several sensor location cases, even the mesh of iFEM is coarser than the FE model. Among the sparse sensor location cases, the X path which is suitable for Fiber Bragg Grating (FBG) sensors has the highest accuracy of the displacement reconstruction. The results reveal a promising prospect in the real-time monitoring based on iFEM for pipes.

Roller-gear-cam (RG-cam) drive systems in machine tool rotary tables provide enhanced rotation accuracy and superior transmission efficiency due to their preloaded design. However, achieving a controllable preload engagement between the roller and RG-cam requires the use of 5-axis CNC machining using a non-formed tool, which inevitably introduces machining errors. Accordingly, this study proposes a comprehensive method for the design, analysis, and manufacture of a precision RG-cam system on a conventional 5-axis CNC machine tool. Notably, the proposed method enables the machining errors to be controlled and minimized. The feasibility of the proposed approach is demonstrated by machining a prototype roller-drive system on a 5-axis CNC machine. The accuracy of the machined RG-cam is evaluated in accordance with the ISO 230-2 standard. It is shown that the forward and reverse rotational errors of the RG-cam over a single revolution are less than 10 s.

To address the challenges of low accuracy and limited generalization in long-tailed fault diagnosis, an adaptive data distribution-based reinforcement learning General Agent is proposed. The method primarily targets more discriminative, domain-invariant feature learning by pre-training the deep Q-network with unlabeled positive samples. Supervisory signals derived from the data’s intrinsic structure enhance class boundary detection while maximizing intra-class feature similarity. Next, empirical data prioritization based on state-action values and TD-error enables efficient utilization of rare but critical experiences, significantly improving sampling efficiency. Concurrently, an adaptive distribution strategy refines a hierarchical reward system by dynamically calibrating the reward function according to real-time accuracy feedback. The deep Q-network, structured with ResNet as the backbone, integrates Efficient Channel Attention (ECA) and Global Attention Mechanism (GAM) to enhance decision-making robustness. Tested on a long-tailed shipboard antenna dataset, the proposed method autonomously identifies fault patterns, demonstrating clear advantages in efficiency, robustness, generalization, and interpretability.

Directional valves are critical functional components in hydraulic systems for diverting the flow of hydraulic oil and controlling oil pressure. Faults of the valves could lead to failure and even serious accidents in hydraulic systems. Fault diagnostic accuracy is directly influenced by the effectiveness of signal processing. Due to the harsh operating environment of hydraulic systems, the condition monitoring signal acquired from the systems contains a large amount of redundant information and environmental noise. This makes the training processes of fault diagnostic approaches time-consuming and inefficient. In this paper, a new hybrid intelligent approach to spool jamming fault diagnostics for hydraulic directional valves is presented. The approach is designed based on the combined strengths of wavelet packet decomposition (WPD), empirical mode decomposition (EMD), variable mode decomposition (VMD), and the multi-layer perceptron (MLP) to improve the efficiency and accuracy of the fault diagnostic process. Five evaluation indices, including processing time (PT), local average accuracy (LAA), stability of accuracy (SA), weighted average of the neuron number (WANN), and robustness of WANN (ROB), are applied to benchmark the approach using ablation experiments. The experimental results show that the hybrid approach has a strong capability to extract fault-related features from the signal and perform fault diagnostics with high accuracy, efficiency, and robustness.

Multipath channel measurement is the foundation of multipath channel modeling and analysis. Recently, the software defined radio (SDR) has provided new ideas for the design of multipath channel measurement systems due to universality and reconfigurability. Therefore, this paper introduces a multipath channel measurement method applicable to various SDR platforms. At first, to enhance the resolution of multipath measurements, a signal processing approach combining the least squares (LS) estimation algorithm within the SDR framework is proposed. Furthermore, to mitigate LS estimation errors stemming from high correlation among baseband signals, a scrambled measurement signal is introduced and its effectiveness is demonstrated through eigenvalue decomposition. Next, the systematic errors of In-phase and Quadrature (IQ) imbalance and bandwidth limitation are further analyzed and addressed within the SDR framework. Finally, the proposed measurement method is implemented based on a practical universal software radio peripheral (USRP) device and validated under simulated and real channels. The proposed measurement method mainly focuses on the baseband signal processing within the software component of SDR, making it applicable to most general USRP devices. The experiment results demonstrate that multipath channel measurement results with high accuracy can be obtained.

The independent metering control system (IMCS) demonstrates high control precision and energy efficiency for advanced electrohydraulic applications but tends to be faulty. Current model-based fault diagnosis methods face challenges in robustness and adaptability to high uncertainties and high nonlinearities of the IMCS. To enhance the safety and reliability of the IMCS, a novel fault detection and isolation method based on adaptive robust observer (ARO) is proposed. To reduce estimation errors, the designed ARO combines the advantages of the robust filter and parameter adaption, which can overcome the impact of uncertainties and nonlinearities. To detect the fault robustly and sensitively, three types of adaptive thresholds are designed according to the spool displacement residuals. To isolate the fault, a fault space is established, and spatial fault points are calculated. The verification experiments show that the proposed method can well estimate the spool displacement while accurately detecting and isolating faults of the IMCS.

The atomic spin-exchange relaxation is the most important limitation of the sensitivity for the optically pump magnetometers operated on non-zero magnetic field environment. In this paper, a method with 2$\pi$ magnetic field pulse modulation is proposed to suppress the spin-exchange relaxation by counteracting the Larmor precession effect caused by the external magnetic field, which allows being operated under non-zero magnetic field. This method applies an additional periodic $2\pi$ pulse magnetic field repeated with the Larmor precession frequency on the conventional Bell-Bloom magnetometer. The theoretical model of the Bell-Bloom magnetometer responding with $2\pi$ pulse magnetic field is established, and the suppression of the spin-exchange relaxation is demonstrated experimentally. This method reduces the spin-exchange relaxation by 91.4% and improves the magnetometer response by 74.0%, which is directly beneficial to the sensitivity of the atomic magnetometer applied in $\mu$T-scale magnetic field.

In this paper, a modified genetic algorithm (GA), Meta-GA, was developed to determine the optimal sensor placement (OSP). Then, the proposed OSP method was compared with the other two GA-based methods. Finally, a 15-month-long structural health monitoring was conducted for Lugou Bridge using the best sensor layout. The deformation monitoring results were compared with point cloud using a four-step method. The damage detection capability of the obtained sensor layout was also verified. The results show that Meta-GA has good computational efficiency and it can save 9.16 %∼28.24 % of optimization time compared to GA. Moreover, the damage sensitivity index obtained from Meta-GA is 12 %∼64 % higher than those of GA-based methods. Furthermore, the error between the monitoring deformation and the point cloud results is almost within 15 %. 90 % of apparent damage locations can be identified based on current sensor data. The research results can support for the structural health monitoring of masonry bridge.

The use of well-fit models enables users to make better decisions and draw accurate insights, so in this article a multi-step procedure for establishing regression functions for experimental data is presented. This was inspired by the need to determine well-fitted models for the results of the study of the relationship between the input factors of the grinding process of planer knives and the measured grinding power, as well as in relation to the knife cutting force. The sharpening process was carried out under various conditions, making it possible to assess their influence on the results and select the most favorable technological parameters to prepare the knives for operation. The equations adjusted by Excel or other basic methods require additional verification if the mathematical formula and values of the model coefficients correspond to the data and the physical meaning. For this purpose, one-, two- and three-factor analyses were performed to eliminate redundant factors and introduce significant interactions between input parameters. Additional analyses of variance and Tukey, Levene and Brown-Forsythe tests were used to determine the general form of the model. Detailed form of nonlinear models was worked out by comparing the distribution of residuals, looking for outliers and evidence of the presence or absence of collinearity, as well as by determining the values of additional model quality indicators, such as Akaike Information Criterion, Cook’s distance and Variance Inflation Factor. The models presented include a categorization variable (grinding type) and are presented graphically in the form of surface plots. With reference to the technological processes studied, a discussion of the physical significance of the developed models and the influence of the input factors on the model output value was conducted. The analyzes additionally include an evaluation of the model factors using prediction slice plots.