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This paper aims to analyze and investigate the performance of an improved fault detection and identification (FDI) method based on multiple criteria, applied to six-switch three-phase inverter (SSTPI)-fed induction motor (IM) drives under both single and multiple open insulated-gate bipolar transistors(IGBT) faults.

This paper proposes an advanced diagnostic method for both single and multiple open IGBT faults dedicated to SSTPI-fed IM drives considering five distinct faulty operating conditions as follows: a single IGBT open-circuit fault, a single-phase open-circuit fault, a non-crossed double fault in two different legs, a crossed double fault in two different legs and a three-IGBT open-circuit fault. This is achieved because of the introduction of a new diagnosis variable provided using the information of the slope of the current vector in (α-β) frame. The proposed FDI method is based on the synthesis and the analysis, under both healthy and faulty operations, of the behaviors of the introduced diagnosis variable, the three motor phase currents and their normalized average values. Doing so, the developed FDI method allows a best compromise of fast detection and precision localization of IGBT open-circuit fault of the inverter.

Simulation works, carried out considering the implementation of the direct rotor flux oriented control in an IM fed by the conventional SSTPI, have proved the high performance of the advanced FDI method in terms of fast fault detection associated with a high robustness against false alarms, against speed and load torque fast variations and against the oscillations of the DC-bus voltage in the case of both healthy and faulty operations.

This work should be extended considering the validation of the obtained simulation results through experiments.

Different from other FDI methods, which suffer from a low diagnostic effectiveness for low load levels and false alarms during transient operation, this method offers the potentialities to overcome these drawbacks because of the introduction of the new diagnosis variable. This latter, combined with the information provided from the three motor phase currents and their normalized average values allow a more efficient detection and identification of IGBT open-circuit fault.

Diagnostics of electrical machines is a very important task. The purpose of this paper is the presentation of coupling three numerical techniques, a finite element analysis, a signal analysis and an artificial neural network, in diagnostics of electrical machines. The study focused on detection of a time-varying inter-turn short-circuit in a stator winding of induction motor.

A finite element method is widely used for the calculation of phase current waveforms of induction machines. In the presented results, a time-varying inter-turn short-circuit of stator winding has been taken into account in the elaborated field-circuit model of machine. One of the time-varying short-circuit symptoms is a time-varying resistance of shorted circuit and consequently the waveform of phase current. A general regression neural network (GRNN) has been elaborated to find a number of shorted turns on the basis of fast Fourier transform (FFT) of phase current. The input vector of GRNN has been built on the basis of the FFT of phase current waveform. The output vector has been built upon the values of resistance of shorted circuit for respective values of shorted turns. The performance of the GRNN was compared with that of the multilayer perceptron neural network.

The GRNN can contribute to better detection of the time-varying inter-turn short-circuit in stator winding than the multilayer perceptron neural network.

It is argued that the proposed method based on FFT of phase current and GRNN is capable to detect a time-varying inter-turn short-circuit. The GRNN can be used in a health monitoring system as an inference module.

The purpose of this paper is to propose a new algorithm for detection of chaos in oscillatory circuits. The algorithm is based on the wavelet transform.

The proposed detection is developed by using a specific measure obtained by averaging wavelet coefficients. This measure exhibits various values for chaotic and periodic states.

The proposed algorithm is applied to signals from autonomous systems such as the Chua’s oscillatory circuit, the Lorenz chaotic system and non-autonomous systems such as the Duffing oscillator. In addition, the detection is applied to sequences obtained from the logistic map. The results are compared to those obtained with a detrended fluctuation analysis and a time-frequency signal analysis based on detectors of chaotic states.

In this paper, a new algorithm is proposed for the detection of chaos from a single time series. The proposed technique is robust to the noise influence, having smaller calculation complexity with respect to the state-of-the-art techniques. It is suitable for real-time detection with delay that is about half of the window width.

On-time fault diagnosis in electrical machines is a critical issue, as it can prevent the development of fault and also reduce the repairing time and cost. In brushless synchronous generators, the significance of the fault diagnosis is even more because they are widely used to generate electrical power all around the world. Therefore, this study aims to propose a fault detection approach for the brushless synchronous generator. In this approach, a novel extension of Relief feature selection method is developed.

In this paper, by taking the advantages of the finite element method (FEM), a brushless synchronous machine is modeled to evaluate the machine performance under two conditions. These conditions include the normal condition of the machine and one diode open-circuit of the rotating rectifier. Therefore, the harmonic behavior of the terminal voltage of the machine is obtained under these situations. Then, the harmonic components are ranked by using the extension of Relief to extract the most appropriate components for fault detection. Therefore, a fault detection approach is proposed based on the ranked harmonic components and support vector machine classifier.

The proposed diagnosis approach is verified by using an experimental test. Results show that by this approach open-circuit fault on the diode rectifier can effectively be detected by the accuracy of 98.5% and by using five harmonic components of the terminal voltage [1].

In this paper, a novel feature selection method is proposed to select the most effective FFT components based on an extension of Relief method, and besides, FEM modeling of a brushless synchronous generator for normal and one diode open-circuit fault.

The purpose of this paper is to obtain an integrated method for inter-turn short circuit fault detection for the cage-rotor induction machine (CRIM) considering saturation effect.

The magnetic equivalent circuit (MEC) is proposed for machine modeling and nonlinear B-H curve is considered for saturation effect. The machine has some differential equations which are converted to algebraic type by trapezoidal method. On the other hand, some nonlinear equations are present due to saturation effect. A set of nonlinear algebraic equation should be solved by numerical method. Therefore, the Newton-Raphson technique is used for equation solving during of the considered time step.

Generally, the operating point of electrical machines is close to the saturation zone due to designing considerations. Moreover, some current and torque harmonics will be produced due to time and space harmonics combination, which cannot be studied when saturation modeling is neglected. Considering both space and time harmonics, a method is proposed for inter-turn short circuit fault detection based on the stator current signatures and the machine performance is analyzed in healthy and faulty cases. In order to obtain the integrated method, two sample machines (two and also four-pole machines) are modeled and finally the accuracy of the proposed method is verified through the experimental results.

The calculations have been done in this work is limited to CRIM considering. However, the presented modeling method can be used for another types of electrical machines by some minor modifications.

Obtaining of an integrated formula for the inter-turn short circuit fault detection which has been presented for first time is the more advantages of present work. Moreover, in order to saturation effect considering, a new method is presented for solving of nonlinear equations which is another novelty of paper.

The purpose of this paper is to propose a new inspecting algorithm for defect detection on PCB circuits.

PCB circuit images were processed by a radon transformation. A Radon histogram was formed and utilized to establish a texture directional characteristic similarity function. Then, a region of the image which contained the same texture directionality feature was segmented. Furthermore, a directionality estimation method is presented. As the circuit was damaged, the directionality was weakened correspondingly. According to principle, the concept of directional intensity was proposed and then used to measure directionality through analysis of the Radon histogram fluctuation. Finally, the defect was detected based on directional intensity.

The method has been applied to an inspecting system used in practice and it achieved a higher accuracy and efficiency in comparison with similar methods.

Although work on highly intensive PCB circuitry inspection and flaw detection is presented, defect classification was not involved although this is also a very important requirement of inspection.

The paper provides a new way to detect PCB circuitry defects based on texture directionality and proposes evaluating the similarity between image texture directionalities using a radon transformation to search the inspected area. As the inspected region was located, the concept of directional intensity was defined to measure texture directionality to identify defects. The new algorithm performs stably and efficiently and is fit for practical application.

This paper aims to investigate the efficacy of teeth flux sensors in detecting, locating and assessing the severity of short-circuit faults in the stator windings of induction machines.

The experimental study involves inducing short-circuit winding turn variations on the induction machine’s stator and continuously measuring the RMS values across teeth flux sensors. Two crucial steps are taken for machine diagnosis: measurements under load operating conditions for fault detection and measurements under no-load conditions to determine fault location and severity.

The experimental results demonstrate that the proposed approach using teeth flux sensors is reliable and effective in detecting, locating and evaluating the severity of stator winding faults.

While this study focuses on short-circuit faults, future research could explore other fault types and alternative sensor configurations to enhance the comprehensiveness of fault diagnosis.

The methodology outlined in this paper holds the potential to significantly reduce maintenance time and costs for induction machines, leading to substantial savings for companies.

This research contributes to the field by presenting an innovative approach that uses teeth flux sensors for a comprehensive fault diagnosis in induction machines. The originality lies in the effectiveness of this approach in providing reliable fault detection, location and severity evaluation.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This study aims to simulate and test the performance of a transmitting and receiving capacitive micro-machined ultrasonic transducer (CMUT). Aimed at detecting demand of the CMUT, a matched integrated adjustment circuit was designed through analyzing processing methods of transducer’s weak echo signal.

Based on the analysis of CMUT array structure and work principle, the CMUT units are designed and the dynamic performance analysis of SIMULINK is given according to the demand of underwater detecting. A transceiver isolation circuit is used to make transmission mode and receiving mode separate. A detection circuit is designed based on the transimpedance amplifier to achieve extraction of high-frequency and weak signal.

Through experimentation, the effectiveness of the CMUT performance simulation and the transceiver integrated adjustment circuit were verified. In addition, the test showed that CMUT with 400 kHz frequency has wider bandwidth and better dynamic characteristics than other similar transducers.

This paper provides a theoretical basis and design reference for the development and application of CMUT technology.

Interlaminar short circuits in turbo generator stators can lead to local damage of the iron core. The purpose of this paper is to model an interlaminar short circuit diagnosis test on an existing structure.

This work presents the modeling of short‐circuited laminations in a stator yoke of a turbo‐generator. A 3D finite element model, associated to a homogenization technique, is used to calculate the short‐circuit current. The diagnosis test known as El Cid has been modelled as well.

Calculation results are compared with the experiment. The same tendency has been observed both in experimental and numerical results.

Additional calculations may be performed (parametric studies) in order to investigate El Cid measuring under different conditions (different material properties, fault position, size), which may lead to a better interpretation of the results.

Modelling of short circuit diagnosis tests under different conditions may help with the interpretation of measuring results, predicting the fault size/seriousness and location. So, only the concerned parts of the stator have to be disassembled and repaired/rebuilt.

It is not easy to model numerically a structure with a short circuit inside, since different dimensions are involved: the fault and the varnish between laminations are much smaller than the stator itself. Thus, homogenization techniques have been used to model the lamination stack region. The combination of this technique with the modelling of the El Cid test constitutes a tool to study this kind of fault and calculate its severity and location in a stator.