This article concerns numerical modeling of the impact of mining operations on fault behavior, carried out on the basis of a calculation program based on the finite element method. It was assumed that the fault is a single discontinuity in the form of a vertically-oriented plane, and the conditions in which surfaces merge are defined by the right of the Coulomb friction. On the one hand, the calculations are related to the fault’s response to additional weight resulting from mining operations, and on the other, they are related to the impact that occurrences in the fault’s plane had on the immediate surroundings of the extraction center. The behavior of the fault was analyzed based on distributions in the plane of shear stress and slip, together with their range and energy dissipated due to friction. In turn, the impact of the fault on its immediate environment was analyzed based on variations in the total energy density of elasticity. The results of numerical modeling made it possible to draw conclusions concerning mining operation in the proximity of tectonic dislocations in the context of seismic hazard’s levels.
An intelligent boundary switch is a three-phase outdoor power distribution device equipped with a controller. It is installed at the boundary point on the medium voltage overhead distribution lines. It can automatically remove the single-phase-to-ground fault and isolation phase-to-phase short-circuit fault. Firstly, the structure of an intelligent boundary switch is studied, and then the fault detection principle is also investigated. The single-phase-to-ground fault and phase-to-phase short-circuit fault are studied respectively. A method using overcurrent to judge the short-circuit fault is presented. The characteristics of the single-phase-to-ground fault on an ungrounded distribution system and compositional grounded distribution system are analyzed. Based on these characteristics, a method using zero sequence current to detect the single-phase-to-ground fault is proposed. The research results of this paper give a reference for the specification and use of intelligent boundary switches.
This paper deals with multiple soft fault diagnosis of nonlinear analog circuits comprising bipolar transistors characterized by the Ebers-Moll model. Resistances of the circuit and beta forward factor of a transistor are considered as potentially faulty parameters. The proposed diagnostic method exploits a strongly nonlinear set of algebraic type equations, which may possess multiple solutions, and is capable of finding different sets of the parameters values which meet the diagnostic test. The equations are written on the basis of node analysis and include DC voltages measured at accessible nodes, as well as some measured currents. The unknown variables are node voltages and the parameters which are considered as potentially faulty. The number of these parameters is larger than the number of the accessible nodes. To solve the set of equations the block relaxation method is used with different assignments of the variables to the blocks. Next, the solutions are corrected using the Newton-Raphson algorithm. As a result, one or more sets of the parameters values which satisfy the diagnostic test are obtained. The proposed approach is illustrated with a numerical example.
The paper deals with a multiple fault diagnosis of DC transistor circuits with limited accessible terminals for measurements. An algorithm for identifying faulty elements and evaluating their parameters is proposed. The method belongs to the category of simulation before test methods. The dictionary is generated on the basis of the families of characteristics expressing voltages at test nodes in terms of circuit parameters. To build the fault dictionary the n-dimensional surfaces are approximated by means of section-wise piecewise-linear functions (SPLF). The faulty parameters are identified using the patterns stored in the fault dictionary, the measured voltages at the test nodes and simple computations. The approach is described in detail for a double and triple fault diagnosis. Two numerical examples illustrate the proposed method.
This paper is devoted to multiple soft fault diagnosis of analog nonlinear circuits. A two-stage algorithm is offered enabling us to locate the faulty circuit components and evaluate their values, considering the component tolerances. At first a preliminary diagnostic procedure is performed, under the assumption that the non-faulty components have nominal values, leading to approximate and tentative results. Then, they are corrected, taking into account the fact that the non-faulty components can assume arbitrary values within their tolerance ranges. This stage of the algorithm is carried out using the linear programming method. As a result some ranges are obtained including possible values of the faulty components. The proposed approach is illustrated with two numerical examples.
The paper presents an analogue circuit testing method that engages the analysis of the time response to a non-periodic stimulus specialized for the verification of selected specifications. The decision about the current circuit diagnostic state depends on an amplitude spectrum decomposition of the time response measured during the test. A shape of the test excitation spectrum is optimized with the use of a differential evolution algorithm and it allows for achieving maximum fault coverage and the optimal conditions for fault isolation. Genotypes of the evolutionary system encode the amplitude spectrum of candidates for testing stimuli by means of rectangle frequency windows with amplitudes determined evolutionarily.
The present article investigates the dynamic behavior of a fully assembled turbogenerator system influenced by misalignment. In the past, most of the researchers have neglected the foundation flexibility in the turbogenerator systems in their study, to overcome this modelling error a more realistic model of a turbogenerator system has been attempted by considering flexible shafts, flexible coupling, flexible bearings and flexible foundation. Equations of motion for fully assembled turbogenerator system including flexible foundations have been derived by using finite element method. The methodology developed based on least squares technique requires forced response information to quantify the bearing–coupling–foundation dynamic parameters of the system associated with different faults along with residual unbalances. The proposed methodology is tested for the various level of measurement noise and modelling error in the system parameters, i.e., 5% deviation in E (modulus of elasticity) and ρ (density), respectively, for robustness of the algorithm. In a practical sense, the condition analyzed in the present article relates to the identification of misalignment and other dynamic parameters viz. bearing and residual unbalance in a rotor integrated with flexible foundation.
One of the most important issues that power companies face when trying to reduce time and cost maintenance is condition monitoring. In electricity market worldwide, a significant amount of electrical energy is produced by synchronous machines. One type of these machines is brushless synchronous generators in which the rectifier bridge is mounted on rotating shafts. Since bridge terminals are not accessible in this type of generators, it is difficult to detect the possible faults on the rectifier bridge. Therefore, in this paper, a method is proposed to facilitate the rectifier fault detection. The proposed method is then evaluated by applying two conventional kinds of faults on rectifier bridges including one diode open-circuit and two diode open-circuit (one phase open-circuit of the armature winding in the auxiliary generator in experimental set). To extract suitable features for fault detection, the wavelet transform has been used on recorded audio signals. For classifying faulty and healthy states, K-Nearest Neighbours (KNN) supervised classification method was used. The results show a good accuracy of the proposed method.
The cohesion and internal friction angle were characterized as quadratic functions of strain and were assumed to follow the Mohr-Coulomb criterion after the yield of peak strength. These mechanical parameters and their variations in post-peak softening stage can be exactly ascertained through the simultaneous solution based on the data points of stress-strain curves of triaxial compression tests. Taking the influence of the fault into account, the variation of strata pressure and roadway convergence with coal advancement, the temporal and spatial distribution of axial bolt load were numerically simulated by FLAC3D (Fast Lagrangian Analysis of Continua) using the ascertained post-peak mechanical parameters according to the cohesion weakening and friction strengthening model. The change mechanism of axial load of single rock bolt as abutment pressure changes was analyzed, through the comparison analysis with the results of axial bolt load by field measurements at a coal mine face. The research results show that the simulated results such as the period of main roof weighting, temporal and spatial distribution of axial bolt load are in accordance with field measurement results, so the validity of the numerical model is testified. In front of the working face, the front abutment pressure increases first and then decreases, finally tends to be stable. A corresponding correlation exists between the variation of axial bolt load and rock deformation along the bolt body. When encountered by a fault, the maximum abutment pressure, the influential range of mining disturbance and the roadway convergence between roof and floor before the working face are all increased. In the roadways along the gob, axial bolt loads on the side of the working face decrease, while the other side one increases after the collapse of the roof. As superficial surrounding rock mass is damaged, the anchoring force of rock bolts will transfer to inner rock mass for balancing the tensile load of the bolts.
A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.
The arc suppression coil determines whether it can effectively extinguish the arc when it is grounded in the neutral non-effective grounding system. An artificial grounding test is an importantway to verify its performance. In this study, 13 substations with the 10 kV system in the Ningxia areawere selected and considered. Based on the artificial single-phase grounding test, the residual current, the compensation current and the off-resonance degree were measured in the arc suppression coil, and the performance of the arc suppression coil in the 10 kV system was verified. The experimental results show that the error of arc suppression coil automatic measurement is large, the off-resonance degree is large, the resistive component in the compensation current is excessive, the harmonic component exists in the compensating current and capacitive current. To solve these problems, this paper puts forward the corresponding countermeasures for reference.
The Bulletin of the Polish Academy of Sciences: Technical Sciences (Bull.Pol. Ac.: Tech.) is published bimonthly by the Division IV Engineering Sciences of the Polish Academy of Sciences, since the beginning of the existence of the PAS in 1952. The journal is peer‐reviewed and is published both in printed and electronic form. It is established for the publication of original high quality papers from multidisciplinary Engineering sciences with the following topics preferred: Artificial and Computational Intelligence, Biomedical Engineering and Biotechnology, Civil Engineering, Control, Informatics and Robotics, Electronics, Telecommunication and Optoelectronics, Mechanical and Aeronautical Engineering, Thermodynamics, Material Science and Nanotechnology, Power Systems and Power Electronics. Journal Metrics: JCR Impact Factor 2018: 1.361, 5 Year Impact Factor: 1.323, SCImago Journal Rank (SJR) 2017: 0.319, Source Normalized Impact per Paper (SNIP) 2017: 1.005, CiteScore 2017: 1.27, The Polish Ministry of Science and Higher Education 2017: 25 points. Abbreviations/Acronym: Journal citation: Bull. Pol. Ac.: Tech., ISO: Bull. Pol. Acad. Sci.-Tech. Sci., JCR Abbrev: B POL ACAD SCI-TECH Acronym in the Editorial System: BPASTS.
The paper presents a methodology for parametric fault clustering in analog electronic circuits with the use of a self-organizing artificial neural network. The method proposed here allows fast and efficient circuit diagnosis on the basis of time and/or frequency response which may lead to higher production yield. A self-organizing map (SOM) has been applied in order to cluster all circuit states into possible separate groups. So, it works as a feature selector and classifier. SOM can be fed by raw data (data comes from the time or frequency response) or some pre-processing is done at first. The author proposes conversion of a circuit response with the use of e.g. gradient and differentiation. The main goal of the SOM is to distribute all single faults on a two-dimensional map without state overlapping. The method is aimed for the development stage because the tolerances of elements are not taken into account, however single but parametric faults are considered. Efficiency analyses of fault clustering have been made on several examples e.g. a Sallen-Key BPF and an ECG amplifier. Testing procedure is performed in time and frequency domains for the Sallen-Key BPF with limited number of test points i.e. it is assumed that only input and output pins are available. A similar procedure has been applied to a real ECG amplifier in the frequency domain. Results prove a high efficiency in acceptable time which makes the method very convenient (easy and quick) as a first test in the development stage.
In rotating machineries, misalignment is considered as the second most major cause of failure after unbalance. In this article, model-based multiple fault identification technique is presented to estimate speed-dependent coupling misalignment and bearing dynamic parameters in addition with speed independent residual unbalances. For brevity in analysis, a simple coupled rotor bearing system is considered and analytical approach is used to develop the identification algorithm. Equations of motion in generalized co-ordinates are derived with the help of Lagrange's equation and least squares fitting approach is used to estimate the speed-dependent fault parameters. Present identification algorithm requires independent sets of forced response data which are generated with the help of different sets of trial unbalances. To avoid/suppress the ill-conditioning of regression equation, independent sets of forced response data are obtained by rotating the rotor in clock-wise and counter clock-wise directions, alternatively. Robustness of algorithm is checked for different levels of measurement noise.
This paper presents mechanical fault detection in squirrel cage induction motors (SCIMs) by means of two recent techniques. More precisely, we have analyzed the rolling element bearing (REB) faults in SCIM. Rolling element bearing faults constitute a major problem among different faults which cause catastrophic damage to rotating machinery. Thus early detection of REB faults in SCIMs is of crucial importance. Vibration analysis is among the key concepts for mechanical vibrations of rotating electrical machines. Today, there is massive competition between researchers in the diagnosis field. They all have as their aim to replace the vibration analysis technique. Among them, stator current analysis has become one of the most important subjects in the fault detection field. Motor current signature analysis (MCSA) has become popular for detection and localization of numerous faults. It is generally based on fast Fourier transform (FFT) of the stator current signal. We have detailed the analysis by means of MCSA-FFT, which is based on the stator current spectrum. Another goal in this work is the use of the discrete wavelet transform (DWT) technique in order to detect REB faults. In addition, a new indicator based on the MCSA-DWT technique has been developed in this study. This new indicator has the advantage of expressing itself in the quantity and quality form. The acquisition data are presented and a comparative study is carried out between these recent techniques in order to ensure a final decision. The proposed subject is examined experimentally using a 3 kW squirrel cage induction motor test bed.