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Given the enormous capital value of power transformers and their integral role in the electricity network, increasing attention has been given to diagnostic and monitoring tools as a safety precaution measure to evaluate the internal condition of transformers. This study overcomes the fault diagnosis problem of power transformers using an ultra high frequency drain valve sensor. A convolutional neural network (CNN) is proposed to classify six types of discharge defects in power transformers. The proposed model utilizes the phase-amplitude response from a phase-resolved partial discharge (PRPD) signal to reduce the input size. The performance of the proposed method is verified through PRPD experiments using artificial cells. The experimental results indicate that the classification performance of the proposed method is significantly better than those of conventional algorithms, such as linear and nonlinear support vector machines and feedforward neural networks, at 18.78%, 10.95%, and 8.76%, respectively. In addition, a comparison with the different representations of the data leads to the observation that the proposed CNN using a PA response provides a higher accuracy than that using sequence data at 1.46%.

In this paper, the amount of SF6 decomposition gases due to the partial discharge (PD) was studied in the SF6 gas-insulated transformer. The long-term PD degradation experiment was performed while controlling the discharge magnitude using the surface discharge, and the gas generation amount was measured by using gas chromatography for SO2F2, SOF2, SO2, CO, and CF4. In addition, to investigate the relationship between the partial discharge energy and the decomposed gas generation amount, partial discharge energy was calculated by a data processing program and converted to the unit of joule per mole. With the finite element method (FEM), the electric field distribution and SF6 gas decomposition mechanism were explained for the partial discharge energy effect on the gas generation. This study helps understand the relationship between the partial discharge energy and the decomposed gas generation ratio with the experimental results and can be used for the diagnosis of PD and maintenance process for the gas-insulated transformers.

In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in threephase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.

This paper proposes a new diagnosis algorithm to detect broken rotor bars (BRBs) faults in induction motors. The proposed algorithm is composed of a frequency signal dimension order (FSDO) estimator and a fault decision module. The FSDO estimator finds a number of fault-related frequencies in the stator current signature. In the fault decision module, the fault diagnostic index from the FSDO estimator is used depending on the load conditions of the induction motors. Experimental results obtained in a 75 kW three-phase squirrel-cage induction motor show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to a zoom multiple signal classification (ZMUSIC) and a zoom estimation of signal parameters via rotational invariance techniques (ZESPRIT).

The classical multiple signal classification (MUSIC) method has been widely used in induction machine fault detection and diagnosis. This method can extract meaningful frequencies but cannot give accurate amplitude information of fault harmonics. In this paper, we propose a new frequency analysis of stator current to estimate fault-sensitive frequencies and their amplitudes for broken rotor bars (BRBs). The proposed method employs a frequency estimator, an amplitude estimator, and a fault decision module. The frequency estimator is implemented by a zoom technique and a high-resolution analysis technique known as the estimation of signal parameters via rotational invariance techniques, which can extract frequencies accurately. For the amplitude estimator, a least squares estimator is derived to obtain amplitudes of fault harmonics, without frequency leakage. In the fault decision module, the fault diagnosis index from the amplitude estimator is used depending on the load conditions of the induction motors. The fault index and corresponding threshold are optimized by using the false alarm and detection probabilities. Experimental results obtained from induction motors show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to the zoom-based MUSIC algorithm.

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

In this paper, a method for detecting broken rotor bar and stator winding fault in a low voltage squirrel-case induction motor using an air-gap flux variation analysis is proposed to develop a simple and low cost diagnosis technique. To measure the leakage flux in radial direction, a radial flux sensor is designed as a search coil and installed between stator slots. The proposed method is able to identify two kinds of motor faults by calculating load condition of motors and monitoring abnormal signals those are related with motor faults. Experimental results obtained on 7.5kW three-phase squirrel-cage induction motors are discussed to verify the performance of the proposed method. Induction motors are widely used electrical machines, for their simplicity of construction and reliability. However, they are subject to failures those may be due to production processes or operating conditions. These unexpected failures cause severe damages in industrial processes. Motor reliability working group have announced that percentage failure in induction motors is typically: stator related (38%), rotor related (10%), bearing related (40%), and others (12%) (1). Recently, many researchers have studied diagnosis techniques to predict motor failures at their incipient stage and decide proper replacement time of induction motors. Most of them are focused on the failure prediction method using abnormal signals of failure patterns of motors from current and vibration signals (2)- (4). Although vibration and current analysis are the most powerful methods for diagnosing motor faults, their sensors are occasionally difficult to install where the environment of industrial field is in poor condition. In addition to this installation problem, there are many low priced low voltage induction motors in the field. Therefore, the diagnosis technique should be easy to install their sensors and low cost comparing to the price of motors. This paper proposes a method for detecting broken rotor bar and stator winding fault in a low voltage squirrel-cage induction motor using a radial flux sensor to develop a simple and low cost diagnosis technique. The sensor is designed as a search coil and installed between stator slots during motor production to measure the leakage flux in radial direction at the air-gap. The proposed method consists of a calculating load condition, finding abnormal signals and monitoring those signals related with motor faults to indentify two kinds of motor failures. To demonstrate the performance of the method, experimental results obtained on 7.5kW three-phase squirrel-cage induction motors are discussed.

First boronization was performed in KSTAR tokamak during 2009 campaign in order to reduce oxygen impurities and to lower the power loss due to radiation. We report the results from the experiences on carborane during the first boronization in KSTAR. After the boronization, H2O and O2 level in the vacuum vessel are reduced significantly. The characteristics of the deposited thin films were analyzed by variable angle spectroscopic ellipsometry, XPS, and AES. ∼1.78 × 1016 cm−2 s−1 of carbon flux on the wall is estimated by using cavity technique.

Laser Thomson Scattering (LTS) is the non-invasive method for measuring the electron temperature and its density, which can be used for the calibrations of electric probes within collisional and magnetized plasmas. For LTS diagnostics in the low-temperature plasmas, one need to special optics for detection of the scattered light with restricting the Rayleigh and Stray lights. For this, one uses the Triple Grating Spectrometer (TGS), which is composed of Rayleigh block (notch filter for Rayleigh light) and double grating filter (DGF). All focusing lenses are used with achromatic doublet configuration for reducing the non-linear optical effects such as spherical aberration, coma, etc. The specifications of the grating and achromatic doublet lens are 1800 gr/mm with the dimensions of 84 mm × 84 mm and 400 mm of focal length with the diameter of 100 mm, respectively. In this configurations, the linear dispersion is given as 1.006 nm/mm. Considering the dimension of Charged Coupled Device (CCD) with the linear dispersion, the LTS system can be measure the electron temperatures of less than 10 eV (in most laboratory plasmas). The initial measurement of LTS measurement and comparative study with single probe are done in Divertor Plasma Simulator (DiPS) with the following plasma parameters; plasma density of 1011–1013 cm−3, electron temperature of 1–4 eV, and the magnetic field of 0.2–1 kG, respectively.

A newly developed diagnosis system using DGA (dissolved gas analysis) was studied for the effective management of oil-immersed transformers. It consists of the measurement system of dissolved hydrogen gas in oil and DGA software based on dissolved gases such as acetylene (C2H2), hydrogen (H2), ethylene (C2H4), methane (CH4), ethane (C2H6), carbon monoxide (CO) and carbon dioxide (CO2). The system for hydrogen gas measurement was designed using semiconductor gas sensor and polymer membrane. Artificial neural network and rule based DGA possibility in the software were used for recognition of fault causes and decision of fault

Since partial discharges weaken the insulation performance of power transformers, the detection of PD is very important for prevention of fatal incidents. This study describes the use of electromagnetic coupler for the partial discharge measurement of transformers. Folded dipole antennas were used as sensors and installed in test chamber for experiments. The spectrum and oscillogram measured in various defect cells are analyzed and showed. And these partial discharges signals are measured at the same time by two types UHF amplifiers with different bandwidth and a conventional PD detector. From these results, decision of defect type is available

A new method for detecting eccentricity and broken rotor bar conditions in a squirrel-cage induction motor is proposed. Air-gap flux variation analysis is done using search coils, which are inserted at stator slots. Using this method, the leakage flux in radial direction can be directly detected. Using finite element method, the air-gap flux variation is accurately modeled and analyzed. From the results of the simulation, a motor under normal condition shows maximum magnetic flux density of 1.3T. On the other hand, the eccentric air-gap condition displays about 1.1T at 60° and 1.6T at 240°. A difference of flux density is 0.5T in the abnormal condition, whereas no difference is detected in the normal motor. In the broken rotor bar conditions, the flux densities at 65° and 155° are about 0.4 T and 0.8T, respectively. These simulation results are coincided with those of experiment. Consequently, the measurement of the magnetic flux at air gap is one of effective ways to discriminate the faulted conditions o...