javad poshtan

In this paper, the performance of the neural network in diagnosing an induction motor situations (safe, bearing outer race, broken rotor bar and stator short circuit faults) has been evaluated using fusion ofthe pre-processed-current-and-voltage-signals information. Moreover, the robustness of the proposed approach is evaluated against the unbalanced power source and dry running of the electro-pump. Results indicate that the proposed neural data fusion has better reliability in this fault detection application, also provides more robust behavior.

In this article , the issue of sensor fault detection and identification with sensory information is considered. This is due to the dependence of successful Fult Detection (FD) method on correct sensory measurements that suffer from various soft sensory faults such as bias, drift, scaling factor, and hard faults that can be detected independently. They are not detectable but can be combined with other sensors. To solve this issue , firstly , a state space m odel for pump subsystem was constructed using the electrical simulation method. Then, the sensory soft faults are modeled and amplified to electro - pump state space model. Both system states and amplified sensory soft faults are then estimated using an Ext ended Kalman filter (EKF) in which nonlinear model of the induction motor is linearized around the estimated states. Information of current, angular velocity (encoder) and pressure sensors are melted for this goal . The efficiency of the method is firstly evaluated through simulation and then experimental results are provided from our laboratory setup . Measured volume cu rrents, flow , and pressure are compared with simulated signals, and results show that the proposed model is able to successfully describe the laboratory system with good precision . These results show that the model can describe the electro - pump dynamic with good precision.

In this paper an observer-based robust fault estimation scheme is proposed for a special class of Lipchitz nonlinear systems where the disturbances and faults are assumed to be coupled with the main system states. In the considered model of system, fault is assumed to enter both of the state and output equations as an unmeasured nonlinear function and coupled with the states. The disturbances and the uncertainties are considered as nonlinear functions coupled with the states. To the best of the authors’ knowledge these conditions have not been previously considered in related papers. In the proposed approach, a Luenberger observer is designed for the estimation of faults and states of system simultaneously. The effect of system disturbances is attenuated with the L2  norm. The necessary conditions for the existence of such observer is expressed in the form of Linear Matrix Inequality. The Lipchitz constant of the nonlinear function is obtained by solving the proposed Linear Matrix Inequality. Finally, the performance of the proposed method is simulated on a three-phase induction motor. The results indicate good performance of the proposed method.

This paper intends to stabilize the flight of an avian-scale flapping robot with articulated. Modeling has been performed using Multibody dynamics, considering a tail.  The equations of motion have been derived from Lagrange equations. Kampf mechanism, inspired by the birds, is used to drive the inner and outer wings with a phase shift. The aerodynamic model has been obtained from applying the blade element theory to the wings divided into twelve elements, considering the inner and outer wing distinction. The aerodynamic forces emerging from the movement of wing elements, in terms of flapping frequency and flight speed, are determined separately. Regarding the flight path angle and effective angle of attack, aerodynamic forces of the entire wings have been achieved in horizontal and vertical axes. The coupling of aerodynamic and dynamic completes the nonlinear time-periodic equations. Due to the impact of the fuselage pitch angle on the flight altitude, the cascade control was used to control fuselage and tail pitch angles in inner loops and altitude in the outer one. Proportional-derivative-integral control has been used to control the performance of the loops, the coefficients of which have been optimally designed

Hyperspectral images provide worthful spectral information for target detection. Since these images are not available in Iran, we use multi-spectral images with approximately 10 bands. Due to differences of hyperspectral and multi-spectral images, a target detection system (using multi-spectral images) is designed in this work. An algorithem for selection and adaptation of an appropriate target detector was proposed to this end. The algorithmic approach assess the target detectors performances on hyperspectral images and designs a systematic target detector system. This system assess which of target detectors of hyperspectral images can be useful for multi-spectral images. Two images from Abadan and Ahvaz acquired by Landsat 8 and Sentinel 2 were selected for our experiments. According to our results, the Kernel based spectral angle mapper shows superior results for targets detection using multi-spectral images. ‌‌

  To increase industrial machines reliability and to prevent shut downs due to faults which cause production loss, fault detection and isolation is very important. There is an increasing request for finding a solution by researchers from industry. Different methods can be used as a solution. Some of them are based on system’s model while some others use experimental data collected from the system. In recent studies hybrid methods are suggested to compensate limitations of individual methods. This approach is briefly reviewed in this article.

Fault propagation analysis is a method based on graph theory used to study the propagation of the effects of faults and disturbances in the system parts. The inclusion of fault propagation and interference of fault and disturbance effects in the design of fault detection algorithms increases reliability and reduces false alarms in critical equipment. In this paper the failure mode and effects analysis (FMEA) method was used to prepare a list of the possible faults and disturbances of each part of a system including of an induction motor and a centrifugal pump. Then a logical model is obtained through the fault propagation analysis to explain the connection between different parts of this system and the propagation of the electrical, vibrational and process effects. This model can be used to consider the propagation of the effects of faults and disturbances in system parts, and the interference of these effects and to select the appropriate effects or sensor configurations required for robust fault detection. The concept of this method is illustrated in this paper by applying this technique to an experimental system.

Signal processing has a key role in signal based fault diagnosis in rotating machinery for finding beneficial discriminating features. Task of Signal processing is conversion of the raw data into beneficial features to facilitate the diagnostic operations. the features should be robust regarding noise and working condition of the machine and simultaneously sensitive to the machine defects. Therefore, assignment of more efficient analyzing techniques in order to achieve more beneficial features of the signal and faster and more accurate fault detection is taken into consideration by researchers. In order to finding such features, the current research applies at first wavelet packet denoising and then applies wavelet packet based Hilbert transform as well as improved Hilbert-Huang transform separately to decompose vibration signal into narrow frequency bands in order to extracting instantaneous frequencies. The findings show that the wavelet packet based Hilbert transform generates better results in comparison to the improved Hilbert-Huang transform in detecting frequencies of the broken rotor bar fault.

This paper uses data fusion based on fuzzy measure and fuzzy integral theory for stator winding inter-turn short circuit fault diagnosis in induction motors. Data fusion be considered in two level: feature level and decision level. Three-phase current signals of induction motor are used for fault diagnosis. Time-domain features are extracted from current signals, and a technique based on fuzzy density is proposed to choose appropriate features. The fuzzy c-mean analysis method is employed to classify different modes. It is used to choose the membership values of each feature for each fault mode. Finally, different features are fused at feature-level using Sugeno fuzzy integral data fusion and at decision-level using Choquet fuzzy integral data fusion to produce diagnostic results. Results show that fuzzy data fusion method performs very well for fault diagnosis in a 4hp laboratory induction motor.

This paper investigates modeling، analysis and design of networked control systems (NCSs)، which are of great interest due to the advantages presented here but also are technically complex because of the challenges of this class of systems such as delay and packet-dropout. Firstly، NCSs with packet dropouts are studied. Markov chains are introduced to describe historical behaviors of packet dropouts. By this method which introduced، general models are derived under single packet transmission protocols. furthermore، controllers are designed to stabilizing the resulting closed-loop systems. Secondly، by above method an inverted pendulum which was controlled in a network with delays، is controlled in a network through packet-dropouts. simulation results show the effectiveness of the above method.

Many real-world applications require minimization of a cost function. This function is the criterion that figures out optimality. In control engineering, this criterion is used in the design of optimal controllers. Cost function optimization has difficulties including calculating gradient function and lack of information about the system and the control loop. In this article, gradient memetic evolutionary programming is proposed for minimization of non-convex cost functions that have been defined in control engineering for the first time. Moreover, stability and convergence of the proposed algorithm are proved. Besides, it is modified to be used in online optimization. To achieve this, the sign of the gradient function is utilized. For calculating the sign of gradient, there is no need to know the cost function shape. The gradient function is estimated by the algorithm. The proposed algorithm is used to design a PI controller for nonlinear benchmark system CSTR (Continuous Stirred Tank Reactor) by online and off-line approaches.

Induction motors are critical components in many industrial processes. Therefore، swift، precise and reliable monitoring and fault detection systems are required to prevent any further damages. The online monitoring of induction motors has been becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose traction motor faults. This paper presents a simple method for the detection of stator winding faults (which make up 38% of induction motor failures) based on monitoring the line/terminal current amplitudes. In this method، fuzzy logic is used to make decisions about the stator motor condition. In fact، fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base، comprising rule and data bases، is built to support the fuzzy inference. Simulation results are presented to verify the accuracy of motor’s fault detection and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.

In this paper، static eccentricity fault detection in induction motors is studied. Two dimensional finite element method (2D-FEM) is used for faultless and eccentric condition modeling in induction motors. Also current and speed signals are compared in two experimental and simulation cases for model validating. For fault detection، fast Fourier transform is used at first. In this method، high order harmonics with small amplitude can alarms the fault occurrence. For this reason، the fault detection process is difficult. To overcome these drawbacks، it is suggested that two test coils contrive around the air-gap. So، any changes in air-gap can be detected easily. Moreover this test coils are used in open circuit case. So، these test coils do not effect on motor dynamics. Also، the results show that modulated voltage can be alarm the fault occurrence، type and percent well.

In most industrial applications, attaining minimum output variance certifies the efficiency of system and results in increasing productivity and decreasing energy consuming. Therefore, minimum variance index is considered as one of the main subjects in control engineering field. A minimum variance controller is an optimal controller that provides (theoretically) the minimum possible variance. Designing this controller, however, requires exact models of both the system and disturbance. Due to existence of interactions among loops, modeling MIMO systems is often complicated. Existence of disturbance is another reason for this complication. In this paper, in order to avoid the need for an analytical model of system, VARX model has been used to simultaneous identification of the plant and disturbance. Then, the estimated model is used for designing a minimum variance controller. In this method, operating data of inputs and outputs of the system are the only requirements for identification of the VARX model. The proposed method has been tested on an experimental benchmark such as a four-tank system which has a nonlinearbehavior. The investigation results show that a minimum variance controller can be designed, based on an identified VARX model, for a MIMO system, without any need for an exact model of the system, while at the same time, capable of providing an acceptable minimum variance compared to that precisely obtained using the model-based ―Interactor-Matrix‖ method.

Electric wheelchairs (EW) experience various terrains surfaces and slopesas well asoccupants with diverse weights. This, in turn, imparts a substantial amount of perturbation to the EWdynamics. In this paper we make use of a two-degree-of-freedom control architecture called disturbance observer (DOB) which reduces sensitivity to model uncertainties while enhancing rejection of disturbances caused due to entering slopes. The feedback loop which is designed via characteristic loci method (CLM) is then augmented with a DOB with a parametrized low-pass filter. According to disturbance rejection, sensitivity reduction, and noise rejection of the whole controller, three performance indices are definedwhich enable us to pick thefilter’s optimal parameters using a multi-objective optimization (MOO) approach callednon-dominated sorting genetic algorithm-II (NSGA-II). Finally, experimental results show desirable improvement in stiffness and disturbance rejection of the proposed controller as well as its robust stability.