Iet Science Measurement & Technology最新文献

This research discusses the effects of large round-off errors on the performance of control charts for means when a process is normally distributed with a known variance and a fixed sample size. Quality control in practice uses control charts for means as a process monitoring tool, even when the data is significantly rounded. The objective of this research is to demonstrate how ignoring the round-off errors and using a standard Shewhart chart affects the quality control of a measured process. The first part of the research includes theoretical calculations for estimating the values of alpha and beta, relating to the unrounded data and the large-rounded data. For the rounded data, normality can no longer be assumed because the data is discrete; therefore, the multinomial distribution is used. The results show that given an in-control process, alpha indicates that false alarms are more frequent, whereas given an out-of-control process, the influence on beta is minor and inconsistent. For some rounding levels, there is a decline in the control chart performances, and in others, there is an improvement. In the second part, a simulation study is used to evaluate the performances of the control chart based on a single sample, checking whether the conclusion (reject or fail to reject) for a sample is consistent for rounded and unrounded data. The results of the simulation match the theoretical calculations.

The asymmetric calibration method is a very interesting technology for differential output probes. It has been proven that the asymmetric calibration method (ACM) can broaden the application scenarios of the electromagnetic field dual probe. However, ACM is not verified on a back-to-back double-loop differential magnetic field probing system. This paper calibrates a back-to-back double-loop differential magnetic field probing system by inserting a connector for creating an asymmetry using ACM. The calibration results show that ACM can be used to calibrate an asymmetric back-to-back double-loop differential magnetic field probing system. The calibration results are further verified by measuring the standing wave magnetic field, and the verification results show that the asymmetric calibration method is effective in eliminating the asymmetry of the back-to-back double-loop differential magnetic field probing system and the work frequency band reaches up to 12 GHz.

The multi-layer screen is the key component in the large synchronous generator end zone. The leakage flux, losses, and temperature of end components are significantly affected by the thickness of multi-layer screen in the synchronous generator. To investigate the influence of multi-layer screen thickness on the end leakage flux, losses, and temperature in the synchronous generator end zone, 1407MVA nuclear power synchronous generator is studied. Three-dimensional transient electromagnetic field model of synchronous generator end zone is established. Three-dimensional transient electromagnetic field in the end zone of 1407MVA synchronous generator with the multi-layer screen is calculated based on the novel iterative method. The flux density of end components is compared and studied in the end zone under the variation of multi-layer screen thickness. Influence of the different thicknesses of multi-layer screen on the losses of the shield plate, screen, finger plate, and stator end core is researched. The losses of end components obtained from 3D end electromagnetic field calculation are applied to the end zone as the heat source in the three-dimensional fluid and thermal coupled field. The temperature distribution of the end components is determined. The accuracy of the calculated results is validated by the experimental values.

Rolling bearings are essential parts in machine equipment and detecting damage in the early stage is crucial for ensuring the safe production and machine life. However, it is difficult to extract weak fault features under strong background noise, discrete harmonic frequency interference and non-stationary service conditions. This investigation proposes a hybrid fault diagnosis approach utilizing transient structure-optimal variational mode decomposition (TS-OVMD) and adaptive group sparse coding (AGSC) for addressing the formidable problem. According to the singular value structure between transient signal and the interference signal, this work investigates the singular value shrinkage (SVS) technique to adaptively obtain the independent components number. Then, we present a transient structure measure (TSM) to adaptively optimize the balance factor. This measure index systematically quantifies the typical characteristics of the bearing fault signal, which can maximize the fault information representation and effectively reduces the useful information loss caused by improper selection of VMD parameters. Finally, a sparse coding model called AGSC is furthermore designed to enhance the fault impulses readability and suppress residual noise based on the sparsity within group property and the TSM. The proposed approach is verified using experimental data and is found to be superiority comparison with the state-of-the-art method.

With the exploration, development, and research of deep-sea resources, there is an urgent need for long-term and continuous observation data of the deep-sea seabed boundary layer. The traditional method of deep-sea seabed survey and sampling based on scientific research vessels has the discontinuity of observation data in space and time scales. There are some problems in the seabed in situ observation method based on the seabed observation network for low mobility and high operation and maintenance costs, restricting the in-depth understanding of the dynamic change process of the deep-sea floor. To solve the above problems, an open and modular data acquisition control system was designed based on an embedded system and signal processing technology. In terms of the physical, chemical, geological, and ecosystem characteristics of the seafloor or near the seafloor boundary layer, various functional sensors and instrumentation were matched to form an independent underwater integrated measurement or experimental device, eventually realizing in situ multiparameter and long-time series observations of the seafloor. The system data acquisition and control test were completed through laboratory experiments, which verified the feasibility of the system design. The research showed important theoretical and technical reference significance for the exploration and development of resources in the submarine boundary layer and the promotion of deep-sea scientific research.

This paper studies the equivalent transmission line model of the pulsed electro-acoustic (PEA) method with its applications. Based on the consistency of acoustic wave behaviour inside lossy acoustic materials and voltage wave propagation in transmission line, an equivalent simulation model of the PEA system is developed, whose reliability is verified by the output from the transducer and amplifier models and the comparison with measured waveforms. For the problem of acoustic impedance mismatch between different modules, simulation indicates that the unequal impedances of semiconducting electrode and sample can affect the amplitude of the measured signal at the upper electrode side, and the reflected acoustic waves caused by the transducer can affect the charge waveform. Further simulation for multi-layer materials finds that the reflected acoustic waves of different samples and sound absorbing module can superimpose on the charge signal. Accordingly, a selection criterion is proposed to avoid the effect of the reflected waves at the interface. As for the acoustic reflection caused by internal charge, it needs to be dealt with sequentially in calibration process, starting from the result inside the sample near ground electrode. The research can provide a foundation for analyzing the acoustic properties of the PEA method.

In this study, a gas sensor array along with intensity modulation of UV light was utilized to discriminate several gases at room temperature. The sensor array was consisted of two interdigitated microelectrodes and TiO₂ nanofibres were electrospun on them and calcined at 540°C for 90 min. One of these sensors was coated by 2-nm Pt using the DC sputtering method and the other one remained uncoated. In each experiment, the sensor array was located at a distance of 30 mm from a 365-nm UV LED. For intensity modulation of UV light, a staircase waveform voltage was applied to the UV LED. The voltage was included of three voltage steps and the measured powers at a distance of 30 mm from the UV-LED were about 450, 560, and 680 µW/cm², respectively. Analytes including acetone, ethanol, methanol, 2-propanol, and carbon monoxide (CO) at various concentrations ranging from 50 to 500 ppm were examined. Three-dimensional Principal Component Analysis mapping was successfully used for the segregation of all examined gases. The examinations revealed that using sensor array along with intensity modulation of UV light is an effective method for discrimination of several analytes at room temperature.

The synchronous closing technology is an effective way to reduce transient current and voltage, prevent equipment failures, and improve power quality. The proposed algorithm, first by considering the coupling voltages between phases and the residual voltages in an uncompensated transmission line, calculates the zero instant of the voltage curves (ZVC instant) across the poles of the circuit breaker (CB) that is ideally the optimum instant to close the CB. Although other studies have utilized ZVC detection by solely considering either coupling or residual voltages. Secondly, the algorithm seeks to account for the mechanical scattering time of the CB and the rate of decrease of dielectric strength (RDDS) by incorporating delay times into the previously calculated delay values. Although other works have investigated the effect of RDDS or mechanical scattering operation time on synchronous switching to some extent, they have not fulfilled any optimization taking both of them into account.By exerting this algorithm, each phase of CB is closed in the ideal optimum closing target (ZVC instant) with a maximum error of one sample, and then, taking into account the CB characteristics, by compensating the RDDS and mechanical scattering time, CB is energized in the optimal time interval, where pre-strike voltages are minimized.

In order to monitor the state of bushing online, an intelligent monitoring system for transformer bushing was developed. A four-in-one sensor integrating hydrogen sensing technology using palladium nickel alloy, pressure sensing technology, wide range temperature sensing, and micro water measurement technology was developed. A three-in-one integrated sensor based on micro current detection technology was developed to realize online monitoring of bushing dielectric loss, capacitance, and partial discharge. The test results show the hydrogen measurement range of sensor is 0 to 10,000 μL/L, and the measurement uncertainty is lower than 10% or 10 μL/L. The pressure measurement range is 0 to 1.0 MPa, and the uncertainty is lower than 0.3%. The temperature measurement range is −40°C to 85°C, and the uncertainty is lower than ± 1°C. The micro water measurement range is 0 to 1000 μL/L, and the measurement uncertainty is lower than ± 5% or 10 μL/L. The dielectric loss and capacitance error increased by one order of magnitude compared to current standards. The resolution of partial discharge is 5 pC. The performance of the device fully satisfies the requirements for online monitoring of transformer bushing. It has been installed in dozens of 330 and 750 kV substations, providing a reliable guarantee for safe operation of transformer bushing.

PV fault diagnosis remains difficult due to the non-linear characteristic of PV output, which makes PV output to be likely disturbed by the ambient environment. This study proposes a novel convolutional extension neural network (CENN) algorithm, which is a jointed architecture based on convolutional neural network (CNN) and extension neural network (ENN), takes advantage of CNN and ENN. The CENN is combined with the symmetrized dot pattern (SDP) analysis method to diagnose the common eight PV array faults. The SDP is used to transform the measured PV signals into the point coordinate feature image; then, the CENN is trained to identify the different PV faults. Experimental results show an obvious improvement in short detection times and high accuracy compared with traditional CNN and the histogram of oriented gradient (HOG) extraction method with support vector machine (SVM), K-nearest neighbours (KNN), and back propagation neural network (BPNN) classifiers, with 95.3%, 94%, 93.5%, and 93.3% accuracy, respectively. Using the proposed CENN, the accuracy can be raised to 97.3%. Additionally, the signals measured by various sensors are collected using programmable logic controller (PLC). The human–machine interface (HMI) and the proposed algorithm are developed using LabVIEW for graphical design. Finally, the information is transmitted to a tablet PC for performing real-time remote monitoring.