Ultrasonic flowmeters have potential for wide application in natural gas and hydrogen flow measurements in China. Accurate measurement is essential; thus, data fusion of acoustic paths is of importance. A data integration method for multi-path flowmeter measurement is introduced and investigated in this study. The novel data integration method is based on the Levenberg–Marquardt algorithm. Computational fluid dynamics has been used to simulate the flows, and a laboratory scale system was established to obtain experimental measurements. The results of the simulations and experiments reveal that the method is able to reduce measurement error compared with four traditional integration methods in flow-rate measuring in a long straight pipe. Furthermore, both the simulation and experiment results validate the integration method for non-ideal flow conditions, such as flow downstream a single elbow or a 180° bend. The relative errors are within 1%, instead of more than 2%, which is typical for traditional methods.

To treat the anal incontinence, a new artificial anal sphincter is put forward. Aiming at the rectal perception function reconstruction and the mechanical compatibility for artificial anal sphincter, this paper regards the huge mobile contraction wave in the typical rectal pressure contraction waves as the main signal of defecation. Meanwhile, by extracting features of rectal pressure signals with the help of wavelet packet analysis, and by adopting the Davies Bouldin index (DB) optimal algorithm, rectal perception function reconstruction is realised through predicting the defecation by means of the SVM classifier. In order to avoid the rectal ischemia necrosis, a three-dimensional finite element model for the rectum and artificial anal sphincter is constructed, with the purpose of analysing the viscoelasticity of the rectum. Then, prony series is deduced by utilising the constitutive equation of rectum, and finite element analysis for artificial anal sphincter is conducted by simulating the contraction and relaxation of the sphincter to generate load. At last, the shift and the stress distribution for the rectum at the time of being oppressed are obtained. This simulative experiment result shows that the artificial anal sphincter can highly accurately predict the defecation and it features favorable biomechanical compatibility.

In this study, the authors propose a robust adaptive algorithm for frequency estimation in three-phase power systems when the voltage readings are corrupted by random noise sources. The proposed algorithm employs the Clarke's transformed three-phase voltage (a complex signal) and augmented complex statistics to deal with both of balanced and unbalanced system conditions. To derive the algorithm, a widely linear predictive model is assumed for the Clarke's transformed signal where the frequency of system is related to the parameters of this model. To estimate the model parameters with noisy voltage reading, they utilise the notions of maximum correntropy criterion and gradient-ascent optimisation. The proposed algorithm has the computational complexity of the popular complex least-mean-squares (CLMS) algorithm, along with the robustness that is obtained by using higher-order moments beyond just second-order moments. They compare the performance of the proposed algorithm with a recently introduced augmented CLMS (ACLMS) algorithm in different conditions, including the voltage sags and presence of impulsive noises and and higher-order harmonics. Their simulation results demonstrate that the proposed algorithm provides improved frequency estimation performance compared with ACLMS especially when the measured voltages are corrupted by impulsive noise.

In this study, a new acoustic emission (AE) sensor-based planetary gearbox (PGB) fault diagnosis method is presented. The method includes a heterodyne-based AE data acquisition system, empirical mode decomposition (EMD)-based AE signal analysis method, and computation of condition indicators (CIs) for PGB fault diagnosis. The heterodyne technique is hardware-implemented to downshift the sampling frequency of AE signals at a rate compatible to vibration analysis. The sampled AE signals are processed using EMD to extract PGB fault features and compute the CIs. The CIs are input into supervised learning algorithms for PGB fault diagnosis. The method is validated on a set of seeded localised faults on all gears: sun gear, planetary gear, and ring gear. The validation results have shown a promising PGB fault diagnostic performance using the presented method.

This study deals with the analysis of the leakage current (LC) in both time and frequency domains, for different configurations of polluted high voltage insulators (i.e. for clean and polluted insulator surfaces). The characteristic parameters of the recorded LC waveforms up to flashover namely the peak value, the phase shift, the total harmonic distortion and the harmonic contents are investigated. It is shown that the LC peak value and the phase shift cannot be a trustworthy indication of the surface activity related to partial discharges and waveform type whereas the harmonic components magnitude can be well correlated with the LC distortions; it is shown that in the early stages, the fifth and seventh harmonics contribute to the distortion of the LC whereas for advanced stage, the third harmonic contribution is the most significant. A time–frequency analysis is also carried out to better understand the LC behaviour. The spectrogram of the different stages of LC activity is computed using Matlab toolbox. The results show that time–frequency analysis can be used as a tool for patterns recognition and classification of the LC.

Thermistor is most widely used sensor in the temperature measurement due to its high sensitivity and fast response. The non-linearity of the thermistor gives rise to several difficulties for on-chip interface, direct digital readout, wireless transmission and so on. Hence, an effective lineariser is needed to overcome the difficulties. In this study, an artificial neural network-based lineariser has been developed for the thermistor connected in operational amplifier circuit. Operational amplifier-based thermistor signal conditioning circuit exhibits a stable temperature–voltage relation over a range of 0–100°C with low linearity. A multilayer perceptron feed-forward neural network is used for non-linearity compensation of thermistor circuit to further improve the linearity. A linearity of ±0.3% is achieved over 0–100°C with high temperature stability. A notable feature of the proposed method is the non-linearity error remains low over the entire dynamic range of the thermistor. The efficacy of the method is established through simulation studies and its practicality demonstrated with experimental results obtained on a prototype unit built and tested.

Measurement of device current during switching characterisation of an insulated gate bipolar transistor (IGBT) requires a current sensor with low insertion impedance and high bandwidth. This study presents an experimental procedure for evaluating the performance of a coaxial current transformer (CCT), designed for the above purpose. A prototype CCT, which can be mounted directly on a power terminal of a 1200 V/50 A half-bridge IGBT module, is characterised experimentally. The measured characteristics include insertion impedance, gain and phase of the CCT at different frequencies. The bounds of linearity within which the CCT can operate without saturation are determined theoretically, and are also verified experimentally. The experimental study on linearity of the CCT requires a high-amplitude current source. A proportional–resonant (PR) controller-based current-controlled half-bridge inverter is developed for this purpose. A systematic procedure for selection of PR controller parameters is also reported in this study. This set-up is helpful to determine the limit of linearity and also to measure the frequency response of the CCT at realistic amplitudes of current in the low-frequency range.

Ions generated by corona discharge of high-voltage direct current (HVDC) transmission lines exist in the space between conductors and the ground. Electric field strength, ion current density, and space charge density are significant electromagnetic environment parameters of HVDC transmission lines. To measure the charge densities at ground level under the HVDC power lines, an aspirator-type charge meter is designed and utilised. The charge meter is calibrated in the ionised field generated by parallel metal mesh and plate. Charge densities on the ground under the unipolar HVDC conductor are measured by the calibrated devices in the laboratory. Experimental results show that reasonable agreement between measured data by the devices and the ones by field mills and ion current plates could be obtained, which establishes a foundation to research the charge characteristics of ions near the ground.

Metal-oxide surge arresters are one of the most important equipments for power system protection against switching and lightning over-voltages. Surge arresters leakage currents increase by the operating time. In this study, the important factors on leakage current variations such as surface contamination, ultraviolet ageing and varistor degradation have been studied. To accomplish this purpose, experimental tests have been performed on various polymer housed surge arresters. Fast Fourier transform analysis has been performed on measured leakage currents. Results show that ultraviolet radiation and varistor degradation affect resistive harmonic components, especially the third and fifth harmonics. Moreover, it is observed that surface contamination has influence on fundamental harmonic variation more than ultraviolet ageing. In addition, combination of ultraviolet radiation and pollution had more effect on leakage currents. The investigation and the discussion of the results can be used to easily analyse arresters condition leading to effective schedule maintenance.

In this study, applied research based on sub-band adaptive filtering (SAF) of electroencephalography/event related potential signal has been conducted. Noisy co-variants of event related potential signals are filtered through sub-band adaptive filters (AFs). SAF with evolutionary techniques (ETs) has been attempted first time. Five ETs, such as particles swarm optimisation, artificial bee colony, cuckoo optimisation algorithm, genetic algorithm, and differential evolution with their variants have been employed for optimisation of SAF. Average computational time and shape measures the difference of ET-based sub-AFs have been improved by decimation in sub-bands before adaptive filtering.

The results from experiments with a screen-printed piezoelectric sensor, mounted on an artificial finger-tip and including a cosmetic covering, are shown to detect surface information from regular texture patterns. For the automatic control of an artificial hand and to feedback information to the amputee, an algorithm has been developed based on the standard deviation (SD) of signal data from the sensor. The SD analysis for texture detection is novel as it uses a combination of arithmetic processes. It windows the data sequentially and calculates the SD of the data in the windows and then averages the SDs. The output from the algorithm is the frequency spectrum of a signal. Plots, from the output of the algorithm, show events that correspond to the cyclic waveforms produced from the regularity of object surface patterns. The results from the algorithm are confirmed with an analysis of the signals using fast Fourier transforms.

A non-linear adaptive state estimator based on the Gauss–Hermite (GH) quadrature rule has been proposed to suit non-linear signal models where the measurement noise covariance remains unknown. The proposed algorithm which may be used for both parameter and state estimation incorporates online adaptation of the measurement noise covariance ( R ) following maximum-likelihood estimation-based method. The GH quadrature approach has been considered so that the proposed filter may inherit the enhanced estimation accuracy as exhibited by its non-adaptive counterpart. The proposed adaptation algorithm, in contrast to some other reported methods, automatically ensures positive definiteness of the adapted measurement noise covariance. The efficacy of the adaptive algorithm over the non-adaptive GH filter has been demonstrated using Monte Carlo simulation and two case studies. Performance comparison has also been carried out with respect to adaptive unscented Kalman filter with the help of same case studies.

Ball bearings are considered as a critical element in various mechanical systems. Vibration signal analysis is very effective method for finding bearing fault. Accelerometers are used to capture the multi-component vibration signal generated in the machine when it is in use. Various methods based on empirical mode decomposition (EMD) have been used for ball bearing fault diagnosis. EMD method usually suffered from the boundary distortion of intrinsic mode function. Classification of ball bearing fault is one of the challenging tasks in the field of mechanical systems. Various classification schemes such as support vector machine (SVM), K-means clustering, extreme learning machine (ELM) have been used for the classification of ball bearing fault. In this study, footprint analysis of Hilbert transform along with the neural network has been done for ball bearing fault analysis. A comparative analysis of the proposed research study has been done with available methods such as SVM and ELM. A high fault classification accuracy has been achieved using the proposed method for detection of ball bearing fault.

In many state-of-the-art signal processing techniques, a signal within sliding-window (SW) is assumed stationary for analysing non-stationary signals. Whenever this assumption is violated, inaccuracies increase to the unacceptable levels. A narrow window-width is recommended for limiting the effect of variations within window, however, it results in poor frequency resolution and demands for increased computational resources. This study presents a simple and efficient algorithm to address amplitude-variation in a signal within SW, thus, allowing time resolution of 1-cycle and frequency resolution of 5 Hz (as per IEC Std. 61000-4-7) with improved accuracy and reliability of the fast Fourier transform of the time-varying signal. The proposed algorithm computes and applies necessary corrections to provide accurate estimates of the integer harmonics for the most recent cycle, irrespective of the number of cycles within the SW. The proposed algorithm is computationally efficient and fast, therefore, can be implemented easily either on digital signal processor or field programmable gate arrays hardware platform. The salient features of the proposed algorithm have been validated on variety of simulated and experimental signals, and have been compared with similar existing techniques in practice nowadays.

Currently flash memory is widely used for data storage of mobile communication and automobile electronics. In this study the control program of multiple-execution has been designed to suppress the components and level of electromagnetic interference (EMI) noise generated during the data writing/reading of flash memory. The method designed by us can reduce the EMI noise levels of writing and reading operations by 4.39 dB and 2.91 dB respectively, while reducing the EMI noise components by 58.3% and 54.5% respectively, such that the noise interference by flash memory can be effectively reduced when it is used for various electronic devices. Especially for the automobile electronic systems, where the reduction of EMI noise interference can assure driving safety, and the reception sensitivity can be enhanced by suppressing the interference of RF module resulted from platform noise.

An interpretation of the meaning of the adjoint system and its variable, as used in the process of shape optimal design of electro-magnetic systems with sensitivity analysis is introduced. Sensitivity formulae for the discrete and continuum approach are compared and the meaning of the adjoint system is discussed. The adjoint system simplifies sensitivity calculations, and provides the link between the objective function and design variables.

Element-free Galerkin method (EFGM) is one of the numerical methods which is used for solving partial differential equations with moving least squares interpolations. This method is based on finite-element method (FEM) on an integral formulation requires only a set of nodes distributed on the analysis domain for weight function construction. No element connectivity is needed. The objective of this study is to present a modified weight function including automatic node generation for improvement of the EFGM calculation accuracy. Numerical examples show that the effect of the proposed weight function on results accuracy. Verification of improved EFGM simulation results is done by FEM.

Large round-off errors may affect efforts to estimate the distribution parameters. The ratio between the standard deviation σ and the scale step h, δ = σ/h, of the measurement instrument, for which rounding off is large when δ < 0.5, determines the significance of the round off. In this study the authors present a new variance interval estimator based on the method of moments (MoM) approach using the bootstrap technique. The authors compare the MoM interval estimator with two a-parametric estimators, the naïve estimator and Sheppard's correction, using simulation. They find that the MoM interval estimator performs better than the a-parametric estimators in terms of coverage probability and interval length, especially for medium and large samples. The MoM interval estimator should be used to compensate for the large round off errors that can occur when using measurement instruments whose scale step is too large.