Iet Science Measurement & Technology最新文献

Rolling element bearings are vital components within rotating machinery, making them a central focus of maintenance in the prognostics and health management sector. This involves closely monitoring their condition to accurately predict the remaining useful life, increasing reliability while minimizing unexpected breakdowns, thereby enabling cost savings through planned maintenance, and enhancing operational stability and security. To achieve this goal, it is necessary to build an online intelligent system for degradation monitoring and failure prognosis by the construction of a robust health indicator and making quantitative measure for bearing degradation. In this paper, an efficient and reliable approach is proposed to estimate the remaining useful life of bearing. A new prediction method is presented by the combination of kernel smoothing density (KS-density) and bidirectional long short-term memory (BiLSTM). Firstly, KS-density smoothens the preliminarily estimated probability distribution function using machinery degradation data. Secondly, the obtained KS-density is used in feed deep learning technique based on BiLSTM models. On this basis, the variation of the signal distribution models between the current faulty state and the normal conditions state is quantified for bearing health assessment. The effective recognition of bearing degradation by the proposed Weibull-based health index is demonstrated through experimental validations utilizing run-to-failure datasets, provided by the centre for intelligent maintenance systems. The comparison with the literature's review show that the prediction results of the proposed approach are more accurate.

To locate and read the complex pointer meter dial for the images with uneven illumination, blurred dial, and tilted dial, this paper firstly proposes an improved BRISK-FREAK algorithm for dial position. Then, combined with the Line Segment Detector (LSD) algorithm, an automatic identification method for complex pointer meter is proposed. The dial of a large number of SF₆ complex pressure pointer meter images are located and the results reveal that the proposed improved BRISK-FREAK algorithm has good adaptability under strong interference. The computational speed of the proposed algorithm is 33% and 17% higher than the Scale-Invariant Feature Transform (SIFT) algorithm and the Binary Robust Invariant Scalable Keypoints (BRISK) algorithm respectively. The positioning success rate of the proposed algorithm is 40%, 64%, and 32% higher than that of the SIFT, Oriented FAST and Rotated BRIEF (ORB) and BRISK algorithms respectively. The reading success rate of the proposed method is 94.5%, which is 19.5%, 39.9% and 14.8% higher than that of the methods based on the ORB, SIFT and BRISK algorithms respectively. It is particularly suitable for application in the actual substations to realize the identification of complex pointer meters.

This paper introduces finite difference delay modelling (FDDM) for computing the shielding effectiveness (SE) of a conductive enclosure with an aperture against electromagnetic pulses. FDDM offers optimal accuracy and stability for analysing complex structures. The time domain electric field integral equation (TD-EFIE) for the conductive enclosure is derived by imposing boundary conditions on the perfect electrical conductor (PEC) surface in the Laplace domain. Time discretization is based on finite differences, and the Laplace-to-Z transform mapping is utilized. Fast Fourier Transform (FFT) is employed to expedite the FDDM solution process. Both frequency domain shielding effectiveness (FD-SE) and time domain shielding effectiveness (TD-SE) are evaluated using this approach. Finally, to validate the accuracy of the proposed method, results are compared with simulations using CST-MWS software and the frequency domain moment method.

Conventional approaches for determining the optimal locations of surge arresters (SAs) can result in an unnecessary increase in the number of these devices in high-voltage substations. This study presents an effective technique for determining the optimal locations of SAs. The lightning back flashover (BF) and switching over-voltages are predicted with the accurate modeling of the transient behavior of all elements of high-voltage substations and transmission lines, considering the effect of environmental conditions. Also, the proposed limiting parameter Monte Carlo (MC-LP) method is utilized to correctly select the probability distribution of the possible strokes for predicting the insulation risk (IR) of a transformer based on transient over-voltage on the transformer end. Therefore, the most appropriate location to install an SA can be determined with a minimum number of calculations using the structural data of the substation, lines connected to it, and the transformer. Simulations are based on experimental results, and the number of calculations significantly decreases using the proposed algorithm. Simulations of the sample network and implementation of the proposed algorithm with MATLAB and EMTP-RV prove the efficiency of the proposed method for optimum SA placement.

Physics-informed neural networks (PINNs) are neural networks (NNs) that directly encode model equations, like Partial Differential Equations (PDEs), in the network itself. While most of the PINN algorithms in the literature minimize the local residual of the governing equations, there are energy-based approaches that take a different path by minimizing the variational energy of the model. It is shown that in the case of the steady thermal equation weakly coupled to magnetic equation, the energy-based approach displays multiple advantages compared to the standard residual-based PINN: it is more computationally efficient, it requires a lower order of derivatives to compute, and it involves less hyperparameters. The analyzed benchmark problems are the single- and multi-objective optimal design of an inductor for the controlled heating of a graphite plate. The optimized device is designed by involving a multi-physics problem: a time-harmonic magnetic problem and a steady thermal problem. For the former, a deep neural network solving the direct problem is supervisedly trained on Finite Element Analysis (FEA) data. In turn, the solution of the latter relies on a hypernetwork that takes as input the inductor geometry parameters and outputs the model weights of an energy-based PINN (or ePINN). Eventually, the ePINN predicts the temperature field within the graphite plate.

Broadband couplers consist usually of multiple coupled lines that are each a quarter wave-length long. Herein, in contrast to conventional methods, a short length of a microstrip line is coupled to a coaxial line. The length of the coupled microstrip line is small compared to the wavelength at the lowest operating frequency. The coupling is weak and the microstrip line is λ/8 at the highest operating frequency. The proposed structure resembles a pair of coupled lines with a linear coupling response. The coupling exhibits a slope of 6 dB/octave over multi-octave bandwidth. The microstrip line is connected to a lumped element compensating network at the coupled port. The compensating network is a low-pass circuit with a slope of −6 dB/octave. The power handling of the coupler is high due to the coaxial line and the weakly coupled microstrip line. Electromagnetic simulations and analytical formulations are presented. The broadband coupler handles nearly 1 kW of input power. Due to the weak coupling of the microstrip-coaxial coupler, the lumped element circuit needs to handle <1.5 W. A high-power coupler has been fabricated for the frequency range of 30 to 500 MHz. It exhibits a coupling response of 56 ± 0.4 dB.

A piezoelectric tube actuator has a number of segments or electrodes. The induced voltage and the piezoelectric voltage, two easy-to-measure electrical signals in piezoelectric tubes, have been used in position estimation of these actuators since 2006 and 1982. However, since introduction, the induced voltage has never been compared with the piezoelectric voltage for piezoelectric tubes’ position estimation. In addition, only linear models have been used to present the relationship between the induced voltage and the position of piezoelectric tubes. In other words, in the literature, it has been practically assumed that (1) the relationship between the induced voltage and the position is linear, and (2) the induced voltage can estimate the position more accurately compared to the piezoelectric voltage. This article assesses and nullifies both these assumptions. In this research, with the use of the experimental data, both aforementioned voltage signals were mapped into the position through linear and nonlinear models. It was shown that the position can be estimated less accurately with the induced voltage compared to the piezoelectric voltage, and the relationship of the position with the induced voltage presents higher and non-negligible nonlinearity compared to the one with the piezoelectric voltage.

In order to solve the problem of remote oil level measurement for unmanned transformers, this paper proposes an online monitoring technology for transformer oil level based on ultrasonic sensors. Subsequently, a finite element model and experimental testing platform were constructed to analyse and verify the influencing factors of the transformer oil level sensors. The results indicated that there is a lower measurement error at 140 kHz in the ultrasonic frequency range of 20–320 kHz, which can be selected as the recommended frequency. The impurities in the oil and the thickness of the tank wall have a slight impact on the accuracy of oil level measurement, and can lead to a decrease in the signal-to-noise ratio of the echo. Furthermore, as the speed of sound increases by about 4 m/s, for every 1°C increase in oil temperature, it is necessary to calibrate the measurement results based on the oil temperature. The test results of actual experimental transformers showed that the designed online monitoring device can achieve high-precision monitoring of transformer oil level, with a relative error generally less than 3%.

This article develops a method for recovering a one-dimensional rough surface profile from scattered wave field, using a single receiver and repeated measurements when the surface is moving with respect to source and receiver. This extends a previously introduced marching method utilizing low grazing angles, and addresses the key issue of the requirement for many simultaneous receivers. The algorithm recovers the surface height below the receiver point step-by-step as the surface is moved, using the parabolic wave integral equations. Numerical examples of reconstructed surfaces demonstrate that the method is robust in both Dirichlet and Neumann boundary conditions, and with respect to different roughness characteristics and measurement noise.

A slight turn-to-turn short-circuit fault in the incipient stage does not trigger fuse protection, and the regular transmission and transformation functions of the transformer within the power grid are not affected very much by the incipient turn-to-turn short-circuit fault. Therefore, the incipient turn-to-turn short-circuit faults are often undetected, resulting in massive accidents. Incipient turn-to-turn short-circuit faults lead to localized overheating in the transformer, which changes the transformer's oil-tank surface temperature (OTST). A thermal simulation model (TSM) is presented. Based on the TSM, OTST data with different load rates and fault parameters are collected. The steady-state and transient characteristics of OTST are analysed by extracting the OTST feature vector and the temperature difference of specific regions. The results show that the growth of the heat production value of faulty coils causes a rise in the OTST; higher faulty coils' locations lead to wider OTST differences; the temperature difference's area S can follow the incipient short-circuit fault in the first 20 min after it occurs, which is faster than the top oil temperature. This study gives insight into the thermal behaviour of OTST, assisting in fault detection and location at the incipient fault stage.