fault detection
در نشریات گروه مواد و متالورژی-
The design of a Spotted Hyena Optimization Algorithm-Variable Parameter Tilt Integral Derivative with Filter (SHO-VPTIDF) controller for improved performance and enhanced devaluation of harmonic components of grid-connected photovoltaic systems is the main objective of the suggested manuscript. The SHO-VPTIDF controller is proposed by reformulating Tilt Integral Derivative Controller with Filter (TIDCF). The TIDCF is characterized by longer simulation time, lower robustness, longer settling time, attenuated ability for noise rejection, and limited use. This research gap is addressed by replacing the constant gains of TIDCF by variable parameter tilt integral derivative with filter. The VPTIDF replaces the constant gains of TIDCF with error varying control parameters to improve the robustness of the system. The photovoltaic system with nonlinearities causes power quality issues and occasional faults, which can be detected by using Levenberg-Marquardt Algorithm (LMA) based machine learning technique. The novelties of the proposed manuscript including improved stability, better robustness, upgraded accuracy, better harmonic mitigation ability, and improved ability to handle uncertainties are verified in a Matlab simulink environment. In this manuscript, the SHO-VPTIDF and the Direct and Quadrature Control based Sinusoidal Pulse Width Modulation (DQCSPWM) method are employed for fault classification, harmonic diminishing, stability enhancement, better system performance, better accuracy, improved robustness, and better capabilities to handle system uncertainties.
Keywords: SHO-VPTIDF, Fault Detection, Harmonic Mitigation, Improved Performance, Enhanced Stability, Non-Ideal Boost Converter -
This study focuses on utilizing image data for statistical process control and improving quality monitoring in manufacturing and service systems. The effectiveness of individual and combined feature extraction methods is evaluated, with the Wavelet-Fourier approach identified as the most suitable. The proposed method not only identifies image processing issues but also provides valuable information for estimating change points, fault locations, and fault sizes. This enables the resolution and prediction of faults, leading to cost and time savings in production. To perform evaluation of the proposed method, an image from a tile production line is subjected to Wavelet transform, followed by Fourier transform on the obtained coefficients. The results demonstrate the superiority of the Wavelet-Fourier method over individual methods such as Fourier transform and Wavelet transform. The proposed method exhibits comparable or improved performance in fault detection and localization compared to similar research. This study highlights the potential of utilizing image data for statistical process control and quality monitoring, offering a comprehensive solution for fault detection and analysis. The findings contribute to advancements in image processing techniques and have practical implications for enhancing quality monitoring in various industries. By leveraging image data, manufacturers can make informed decisions, enhance process performance, and improve overall product quality.Keywords: Quality performance, Imaging technologies, Feature Extraction, Statistical process control, Online process monitoring, Wavelet-Fourier Method, Fault detection, localization
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In this paper, a new online technique for Hall Effect sensor fault diagnosis in brushless DC (BLDC) motor is proposed. The proposed technique is based on phase current waveform analysis and does not need any Hall sensor information. The normalized phases current values are analyzed per and post-sensor fault in every cycle. Using a definition of suitable conditions and threshold values for normalized currents values, all sensor fault types (i.e. set to 0 and 1) could be detected and located online effectively. The main contribution of this paper is introducing an online BLDC sensor fault detection and location technique under low-speed operation and transient conditions. Simulation results show the effectiveness of the proposed technique in all of the sensor faults types diagnosis without any sensor output value information. Two different types of BLDC motors are considered for fault diagnosis using the proposed technique. Simulation results during starting and low-speed operations of BLDC motor are well confirmed by the experimental results.Keywords: Brushless DC Motor, fault detection, Hall sensor fault, Normalized Current, Online Location
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Fault detection and its restoration is the major challenge for the smooth functioning of the Multi-Level Inverter (MLI). In this paper, fault detection and its clearance scheme for an Open Circuit (OC) fault on a 3-level 5-level Cascaded H-Bridge Multi-Level Inverter (CHBMLI) has been developed and tested to improve the reliability and suitability of the system. An accurate and fast detection, isolation and bypassing of faulty bridges enhance the reliability, suitability, and acceptability of CHBMLI in any domestic, industrial drive applications. To reschedule the line voltage and current value close to the pre-fault level, a Neutral Point Shift (NPS) technique is presented in this paper. The desired output voltage is governed by Level Shift Pulse Width Modulation (LSPWM) technique. The proposed scheme is developed in MATLAB/Simulink environment and results are validated by using Opal-RT simulator. Simulation results has confirmed the performance and Opal-RT simulator results shows feasibility and applicability of the proposed scheme.
Keywords: Cascaded Multi-level Inverter, Level Shift PWM Technique, Fault Detection, Fault Tolerant, Neutral Point Shift Technique -
In this paper, a new fast and accurate method for fault detection, location and classification on multi-terminal direct current (MTDC) distribution networks connected to solar distributed generation and loads presented. Some issues such as DC resources and loads expanding, and try to the power quality increasing have led to MTDC networks' development. It is important to recognize the fault type in order to continue service and prevent further damages. In this method, a circuit kit is connected to the network. Fault detection is performed with the measurement of the current of the connected kits and the traveling-waves of the fault current and applying it to a mathematical morphology filter, in the Fault time. Determine the type and location of faults using a mathematical morphology filter, circuit equations and current calculations. DC series and ground arc faults are considered as DC distribution network disturbances. The presented method was tested in a solar DC network connected to energy storages and solar resources with many faults. The results illustrate the validity of the proposed method. The main advantages of the proposed fault location and classification strategy are higher speed and accuracy than conventional approaches. The fault location error of the presented algorithm is less than 6.5 percent in the worst case. This method robustly operates to changing in sampling frequency [0.5-50 KHz], fault resistance [0.005-120 Ohm], and works very well in high impedance fault.Keywords: MTDC solar network, fault detection, classification, Mathematical morphology, current injection kit, on-line phaselet
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Due to widespread usage of induction motor (IM) in various industries, the requirement for its condition monitoring have been considerably raised. It is essential to detect faults that happened in IMs in a short time with high accuracy because they may cause considerable financial losses. Bearing faults contribute to a large percentage of IM failures. In this paper, vibration signal is analyzed for getting reliable indicator for faulty modes of the bearings of the IMs. The proper direction for measuring the vibration signal is analyzed first. This analysis shows that the fault-related vibration frequency components along the Z-axis, i.e. the axis perpendicular to the motor's installing surface, usually have the largest magnitude. Thus, it is recommended to measure the vibration signal in the Z-axis. Then, the bearing fault diagnosis using the vibration signal is investigated in various scenarios. The results confirm that the vibration indicators are not sensitive to environmental parameters like temperature and also load variation of the IM but the severity of the fault has a considerable influence on them.Keywords: Induction motor, Bearing fault, fault detection, Analyzing vibration
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Photovoltaic (PV) systems have been gaining great attention during the last decade. One of the main faults in PV arrays is partial shading, affecting its electrical parameters. Failure to detect this fault may result in optimal generated power reduction and hot spotting leading to damage to cell encapsulant and second breakdown. This paper develops a partial shading detection algorithm that only requires the available measurements of array voltage and current. By quantifying the wave-shape of the super-imposed component of PV array power by the skewness function, it can discriminate a partial shading condition from the short-circuit and high-resistance faults. The developed scheme is implemented in a central intelligent electronic device and does not require a communication link and training data set. The merits of the proposed partial shading detection algorithm are demonstrated through several fault scenarios using a 5×5 grid-connected PV generation system.Keywords: Fault Detection, Photovoltaic, partial shading, Skewness
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Helicopter unmanned aerial vehicle (HUAV) are an ideal platform for academic researchs. Abilities of this vehicle to take off and landing vertically while performing hover flight and various flight maneuvers have made them proper vehicles for a wide range of applications. This paper suggests a model-based fault detection and isolation for HUAV in hover mode. Moreover in HUAV, roll, pitch and yaw actuator faults are coupled and affect each other, hence, we need a method that decouples them and also separates fault from disturbance. For this purpose, a robust unknown input observer (UIO) is designed to detect bias fault and also catastrophic fault such as stuck in actuators of HUAV. The robust UIO isolates roll and pitch actuator faults from yaw actuator fault. The novelty of this manuscript is the design of two UIO observers to detect and decouple the faults of helicopter actuators, one for lateral and longitudinal actuators and the other for pedal actuator. Also, the proposed method is compared with extended Kalman filter (EKF). Simulation results show effectiveness of the proposed method for detection and isolation of actuator faults with less number of observers and it is able to decouple fault and disturbance effects.Keywords: Helicopter Unmanned Arial Vehicle, fault detection, Fault Isolation, Unknown Input Observer, Actuator Faults
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In recent decades, use of solar systems is developed and it is used in many small applications even in home appliances. DC micro grids with centralized solar generation are widely interested for various power applications due to their advantages over traditional AC power distribution systems with respect to power density and power distribution efficiency. On the other hand, protection of these kind of systems against faults are hard to diagnose. It is difficult to extinguish these arc faults via conventional circuit breakers due to the lack of natural zero-crossing of DC current. To overcome of this problem, this article proposed a new smart relay for detecting and clearing faults in solar networks that can gather data of all elements in grid and transmit data to a central control system in a SCADA system by using IEC61850 standards.
Keywords: SCADA, IED, IEC61850, solar, DC microgrid, Fault Detection -
Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibration signals and are collected using experimental test rig for different input parameters like load, speed and bearing conditions. These features are ranked using two techniques, namely Decision Tree (DT) and Randomized Lasso (R Lasso), which are further used to form training and testing input feature sets to machine learning techniques. It uses three ensemble machine learning techniques for AFB fault classification namely Random Forest (RF), Gradient Boosting Classifier (GBC) and Extra Tree Classifier (ETC). The impact of number of ranked features and estimators have been studied for ensemble techniques. The result showed that the classification efficiency is significantly influenced by the number of features but the effect of number of estimators is minor. The demonstrated ensemble techniques give more accuracy in classification as compared to tuned SVM with same experimental input data. The highest AFB fault classification accuracy 98% is obtained with ETC and DT feature ranking.Keywords: Anti-friction Bearing, Ensemble Learning, Vibration Signal, Fault Detection
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This study examined and presents an effective method for detection of failure of conductor bars in the winding of rotor of induction motor in low load conditions using neural networks of radial-base functions. The proposed method used Hilbert method to obtain the stator current signal push. The frequency and signal amplitude of the push stator were used as the input of the neural network and the network outputs were rotor fault state, and the number of conductive bars with broken fault. Moreover, particle-swarm optimization algorithm was used to determine the optimal network weights and neuron penetration radius in the neural network. The results obtained from the proposed method showed the optimal and efficient performance of the method in detecting conductive bars broken fault in induction motor in low load conditions.Keywords: Fault Detection, Induction Motor, Hilbert Transform, Neural Network, Particle-Swarm Optimization
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The aim of this work is the application of the phase variation in vibration signal for fault detection on rotating machines. The vibration signal from the machine is modulated in amplitude and phase around a carrier frequency. The modulating signal in phase is determined after the Hilbert transform and is used, with the Fast Fourier Transform, to extract the harmonics spectrum in phase. This method is first validated on a simulator of vibration then used for detecting potential faults on a rotor of a centrifugal compressor.Keywords: Phase modulation, Vibration analysis, Hilbert Transform, Fault detection
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This paper proposes a reduct construction method based on discernibility matrix simplification. The method works with genetic algorithm. To identify potential problems and prevent complete failure of bearings, a new method based on rule-based classifier ensemble is presented. Genetic algorithm is used for feature reduction. The generated rules of the reducts are used to build the candidate base classifiers. Then, several base classifiers are selected according to their diversity and the scale of them. Weights of the selected base classifiers are calculated based on a measure of support rate. The classifier ensemble is constructed by the base classifiers. The accuracy reached 98.44% which is 4.5% higher than that of the three base classifiers.Keywords: Fault Detection, Bearing, Classifier Ensemble, Genetic Algorithm
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In some statistical process control applications, the process data are not Normally distributed and characterized by the combination of both variable and attributes quality characteristics. Despite different methods which are proposed separately for monitoring multivariate and multi-attribute processes, only few methods are available in the literature for monitoring multivariate-attribute processes. In this paper, we develop discriminant analysis technique for monitoring the mean vector of correlated multivariate-attribute quality characteristics in the first module. Then in the second module, a novelty approach based on the combination of artificial neural network (ANN) and discriminant analysis is proposed for detecting different mean shifts. The proposed approach is also able to diagnose quality characteristic(s) responsible for out-of-control signals after detecting different step mean shifts. A numerical example based on simulation is given to evaluate the performance of the proposed methods for detection and diagnosis purposes. The detecting performance of the second module is also compared with the extended T2 control chart and with the extension of an ANN in the literature. The results confirm that the proposed method outperforms both methods.Keywords: Discriminant Analysis, Multivariate, attribute, NORTA Inverse, Neural Network, Fault Detection, Fault Diagnosis
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This paper investigates the fault detection of a micro parallel plate capacitor subjected tononlinear electrostatic force. For this purpose, Thau observer, which has demonstrated good performance in fault detection of nonlinear systems,as well asgoverning nonlinear dynamic equation of the capacitor,is presented. Upper and lower threshold for fault detection have been obtained. The robustness of the observer to noise, uncertainty as well as sensitivity to faults wasinvestigated. Finally, simulation results for fault detection of the capacitor are obtained and the ability of the observer in the vicinity of dynamic pull-in voltage has been checked.Keywords: Fault detection, Thau observer, nonlinear force, micro, parallel plate capacitor, noise, uncertainty
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Split-hom together structure is a new heavy load rack structure, and its reliability needs to be verified. Through analyzingmultimodal hom-connection principle from the perspective of bionics and contact mechanics, the article establishes the finite element and mathematical model of the rack. Modal analysis has been done for the finite element model in the prestress state, whose results indicate the reliability of the rack. While numerical simulation is applied to the mathematical model of the rack for the first time in the working state. The results show that the deformations in two ways are almost at the same and meet the demand. Meanwhile, through the minimum frequency of the modal analysis is different from the result of the numerical simulation, the frequency in two states is higher than the workingfrequency. So, the results validate the reliability of the design on split-hom together rack.Keywords: Fault Detection, Thau Observer, Nonlinear Force, Micro, Parallel Plate Capacitor, Noise, Uncertainty
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