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In the era of globalization, manufacturing industries are facing intense pressure to prevent unexpected breakdowns, reduce maintenance cost and increase plant availability. Induction motors are the most sought-after prime movers in modern-day industries due to their robustness. Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. This paper presents the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN)-based system to diagnose the vibration and Instantaneous Power (IP)-based responses of rolling element bearings and broken rotor bars in an induction motor. The dimensionality of the extracted features was reduced using Principal Component Analysis (PCA) and thereafter the selected features were ranked in order of relevance using the Sequential Floating Forward Selection (SFFS) method for reducing the size of input features and finding the most optimal feature set. A comparative analysis of the effectiveness of SVM and ANN is carried out using statistical parameters extracted from vibration and IP signals. The highest accuracy of 92.5% and 98.2% was achieved for vibration and IP signatures, respectively, using the proposed SFFS-based feature selection technique and ANN classification method. The results reveal that ANN has better performance than SVM and the proposed strategy can be used for automatic recognition of machine faults. The use of this type of intelligent system helps in avoiding unwanted and unplanned system shutdowns due to the failure of the motor.

Control of induction motor due to its nonlinear property is one of the most popular research topics recently. Vector control based algorithms which are used for proper functioning estimate the rotor speed and magnetic flux. In many applications, flux and speed are estimated without using sensors. Thus, in these cases, it is necessary to obtain speed and flux by measuring voltage and current. In this paper, a nonlinear observer for estimating motor parameters based on Lyapunov function is presented. Using the Lyapunov theory, inputs regulating system and control system are earned which improve system performance. Simulation and experimental results are included to validate the effectiveness of the proposed control scheme under possible uncertainties and different reference trajectories. These results show the advantages of the proposed observer in control of induction motor transient performances. The merits of the proposed control system are also indicated comparing with a traditional optimal control system.

This paper proposes a hybrid recurrent neuro-fuzzy (RNF) architecture for rotor speed regulation of indirect field oriented controlled (IFOC) induction motor (IM) drive. This approach incorporates Takagi–Sugeno–Kang (TSK) model-based fuzzy logic (FL) laws with a four-layer artificial neural networks (ANNs) scheme. Moreover, for the proposed RNF an improved self-tuning method is developed based on the IM theory and its high performance requirements. The principal task of the tuning method is to adjust the parameters of the FL in order to minimize the square of the error between actual and reference output. The convergence/divergence of the weights is discussed and investigated by simulation.

In this paper, the hybrid direct torque control (DTC) technique is proposed for controlling the speed of the induction motor (IM). The hybrid technique is the combination of an enhanced firefly algorithm (FA) and the adaptive neuro fuzzy inference system (ANFIS) technique. The performance of the FA is improved by updating the randomized parameter. Here, the genetic algorithm (GA) is utilized for updating the parameter and improved the performance of the FA. Initially, the actual torque and the change of toque are applied to the input of the enhanced FA and form the electromagnetic torque as a dataset. The output of the enhanced FA is given to the input of the ANFIS which is determined from the output of interference system. The dynamic behavior of the IM is analyzed in terms of the parameters such as the speed, torque, flux, etc. Based on the parameters, the motor speed is controlled by utilizing the proposed technique. Then the output of the ANFIS is translated into the stator voltage which is given to the input of the support vector machine (SVM). After that, the control signal is generated for controlling the speed of the IM. The proposed hybrid technique is implemented in the Matlab/Simulink platform. The performance analysis of the proposed method is demonstrated and contrasted with the existing techniques such as without controller, particle swarm optimization (PSO)-based ANFIS and FA-ANFIS controller.

In this paper, an effective neuro-fuzzy controller (NFC) technique has been proposed to control the induction motor torque and flux. The NFC hybrid technique is the grouping of the neural network (NN) and fuzzy logic controller (FLC), which generated the target voltages with the corresponding input flux and torque. The novelty of the proposed hybrid technique is highly flexible in nonlinear loads, convenient user interface and logical intellectual and permitting for integrated controlling schemes. Here, the FLC generates the training dataset of the NN technique based on the logical rules. The generated dataset contains the information about the flux and torque deviation parameters and the corresponding reference voltage parameters. The NN has been trained based on training dataset and the testing time which produces the optimal reference voltage parameters depends on the variation of the torque and flux parameters. By using the output of the NFC technique, the space vector modulation (SVM) develops the appropriate control pulses to the five-level inverter and the inverter generates the output voltage signal to the induction motor. The proposed method is designed in the MATLAB/Simulink platform and the outputs are verified through the comparison analysis with the existing techniques.

This paper proposes an efficient fuzzy logic-based fault detection scheme for diagnosing the inter-turn short-circuit (ITSC) faults in induction motors (IMs). The proposed approach utilizes the fast Fourier transforms (FFTs) and wavelet packet transform (WPT) for this detection of fault. To improve the efficiency and secure the operation, the proposed approach is detecting the fault in online manner. The WPT is utilized to extract the stator current signal into time-frequency domain characteristics. The variation in the amplitude of the vibration spectrum at different characteristic frequencies by FFT is utilized to identify the stator ITSC. The vibration signal is dignified by a MEMS accelerometer. The performance of the fuzzy logic fault detector (FLFD) for online condition is monitored with stator current, vibration and input speed. The performance of the proposed approach is performed at MATLAB/Simulink working site, and then the performance is compared to other existing works. The accuracy, precision, recall and specificity of the proposed approach are analyzed. Similarly, the statistical measures like root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (MBE) and consumption time are analyzed.

From a computational point of view, in nonlinear dynamical systems, attractors can be regarded as self-excited and hidden attractors. Self-excited attractors can be localized numerically by a standard computational procedure, in which after a transient process a trajectory, starting from a point of unstable manifold in a neighborhood of equilibrium, reaches a state of oscillation, therefore one can easily identify it. In contrast, for a hidden attractor, a basin of attraction does not intersect with small neighborhoods of equilibria. While classical attractors are self-excited, attractors can therefore be obtained numerically by the standard computational procedure. For localization of hidden attractors it is necessary to develop special procedures, since there are no similar transient processes leading to such attractors.

At first, the problem of investigating hidden oscillations arose in the second part of Hilbert's 16th problem (1900). The first nontrivial results were obtained in Bautin's works, which were devoted to constructing nested limit cycles in quadratic systems, that showed the necessity of studying hidden oscillations for solving this problem. Later, the problem of analyzing hidden oscillations arose from engineering problems in automatic control. In the 50–60s of the last century, the investigations of widely known Markus–Yamabe's, Aizerman's, and Kalman's conjectures on absolute stability have led to the finding of hidden oscillations in automatic control systems with a unique stable stationary point. In 1961, Gubar revealed a gap in Kapranov's work on phase locked-loops (PLL) and showed the possibility of the existence of hidden oscillations in PLL. At the end of the last century, the difficulties in analyzing hidden oscillations arose in simulations of drilling systems and aircraft's control systems (anti-windup) which caused crashes.

Further investigations on hidden oscillations were greatly encouraged by the present authors' discovery, in 2010 (for the first time), of chaotic hidden attractor in Chua's circuit.

This survey is dedicated to efficient analytical–numerical methods for the study of hidden oscillations. Here, an attempt is made to reflect the current trends in the synthesis of analytical and numerical methods.

This work deals with the bifurcation phenomena that occur during the open-loop operation of a single-fed three-phase wound rotor induction motor. This paper demonstrates the occurrence of saddle-node bifurcation, hysteresis, supercritical saddle-node bifurcation, cusp and Hopf bifurcation during the individual operation of this electromechanical system. Some experimental results associated with the bifurcation phenomena are presented.

In this paper, a novel fractional-order control strategy for the SCARA robot is developed. The proposed control is composed of ${\mathrm{\text{PI}}}^{𝜗}$ and a fractional-order passivity-based adaptive controller, based on the Caputo–Fabrizio and Atangana–Baleanu derivatives, respectively; both controls are robust to external disturbances and change in the desired trajectory and effectively enhance the performance of robot manipulator. The fractional-order dynamic model of the robot manipulator is obtained by using the Euler–Lagrange formalism, as well as the model of the induction motors which are the actuators that drive their joints. Through simulations results, the effectiveness and robustness of the proposed control strategy have been demonstrated. The performance of the fractional-order proposed control method is compared with its integer-order counterpart, composed of the PI controller and the conventional passivity-based adaptive controller, reported in the literature. The performance comparison results demonstrate the superiority and effectiveness of the fractional-order proposed control strategy for a SCARA robot manipulator.

This study presents a novel neuro-fuzzy (NF)-based auto-tuning proportional integral controller (NFATPI) for accurate speed control, and to ensure optimal drive performances of the indirect field controlled induction motor drive, under system disturbances and uncertainties. The training mechanism of the proposed NF have been developed and illustrated through mathematical formulations. Then, the NF parameters have been updated on-line using a suitable training algorithm. The learning rates of the NF are derived on the basis of the discrete Lyapunov function is also illustrated, in order to confirm the stability and the performance of prediction of the proposed NFATPI. The simulation results confirm the effectiveness of the strategy NFATPI as a robust controller for high performance industrial motor drive systems.

In this paper, a modified sinusoidal pulse width modulation (MSPWM) technique and a modified single-phase H-bridge seven-level inverter is proposed. The switching pulses for the proposed seven-level inverter are generated using a single triangular carrier waveform, a fully rectified sinusoidal signal, and three stepped reference signals (Uref1, Uref2 and Uref3). Using optimization technique, the magnitude of the stepped reference signal is determined so that the total harmonic distortion (THD) of the output voltage waveform is minimum and the fundamental component, RMS value of the voltage is improved for a given modulation index Ma as compared to the sinusoidal pulse width modulation (SPWM). By the implementation of the new scheme, the seven-level of the inverter output voltage level (+Vdc, +2Vdc/3, +Vdc/3, 0, −Vdc, −2Vdc/3, −Vdc) is obtained for any given modulation index. Similarly, if only two stepped reference signals are used then the inverter will act as a five-level inverter for any modulating index Ma. The proposed MSPWM and seven-level inverter are simulated on MATLAB/SIMULINK for R, R-L load and on a single-phase capacitor-start and capacitor-start-run Induction Motor.

This research intends to establish a random processes model of hitch degree development of induction motor ball bearings based on impact inspections. The variance function and the mean function of the random process are studied for establishing an approximate folding line method and an extrapolation method which can be used to predict the remnant life of the ball bearings. The methods established are verified with the data collected from the petroleum industries utilizing the motor bearings.

Speed estimation of speed sensorless has become major hotspot of modern high performance AC speed regulation; the vector control method is analyzed in deep. Based on voltage model as the basis, the introduction of reference value compensation strategy ensures that the motor can measure rotor speed accurately at low speed. In hardware, the motor control system composed of double DSP control board and two level inverters is designed. In Double DSP control board composed of the TMS320LF2407A and TMS320VC33, high floating point computing power of VC33 solve the problems of programming and calculation precision, the use of hardware features 2407 of its own and combined power system simulation to realize fast communication, sampling function. Finally, the simulation data are analyzed in a case, draw the error parameter curve, and verify the correctness of the velocity measurement method of the speed sensorless vector control.

It is difficult to establish an exact mathematical model for the induction motor and the robustness is poor of the vector control system using PI regulator. This paper adopts the linear active disturbance rejection controller (LADRC) to control inductor motor. LADRC doesn't need the exact mathematical model of motor and it can not only estimate but also compensate the general disturbance that includes the coupling items in model of motor and parameters perturbations by linear extended state observer (LESO), so the rotor flux and torque fully decouple. As a result, the performance is improved. To prove the above control scheme, the proposed control system has been simulated in MATLAB/SIMULINK, and the comparison was made with PID. Simulation results show that LADRC' has better performance and robustness than PID.