Majid Poshtan

Detection of broken rotor bars in induction motors using nonlinear Kalman filters.

This paper presents a model-based fault detection approach for induction motors. A new filtering technique using Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) is utilized as a state estimation tool for on-line detection of broken bars in induction motors based on rotor parameter value estimation from stator current and voltage processing. The hypothesis on which the detection is based is that the failure events are detected by jumps in the estimated parameter values of the model. Both UKF and EKF are used to estimate the value of rotor resistance. Upon breaking a bar the estimated rotor resistance is increased instantly, thus providing two values of resistance after and before bar breakage. In order to compare the estimation performance of the EKF and UKF, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. Computer simulations are carried out for a squirrel cage induction motor. The results show the superiority of UKF over EKF in nonlinear system (such as induction motors) as it provides better estimates for rotor fault detection.

Model-based fault detection in induction Motors

In this paper a model-based fault detection method for induction Motors is presented. A new filtering technique based on Unscented Kalman filters and Extended Kalman filters, is utilized as a state estimation tool in broken bars detection of induction motors. Using the merits of these recent nonlinear estimation tools UKF and EKF, rotor resistance of an induction motor is estimated only by the sensed stator currents and voltages information. In order to compare the estimation performances of EKF and UKF, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. The results show the superiorly of UKF over EKF in highly nonlinear systems, as it provides better estimates of which is most critical for rotor fault detection.

Experimental validation on stator fault detection via fuzzy logic

The detection of faults in induction motors is 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 induction motor faults. This work presents a reliable method for the detection of stator winding faults based on monitoring the line 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. Therefore, this paper applies fuzzy logic to induction motors fault detection and diagnosis. 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. The induction motor condition is diagnosed using a compositional rule of fuzzy inference. Experimental results are presented in terms of accuracy in the detection motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.

Bearing fault detection in induction motor using pattern recognition techniques

In this paper a procedure based on pattern recognition technique is presented for fault diagnosis of rolling element bearings through artificial neural networks (ANN). The artificial neural networks are trained with a subset of the experimental data for known machine conditions. The networks are tested using the remaining set of data. In this method the characteristic features of time and frequency domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN. The features are obtained from direct processing of the signal segments using very simple preprocessing. Three different cases; healthy, inner race defect, and outer race defect is classified using the proposed algorithm. The obtained results indicate that using time-domain features can be effective in the diagnosis of various motor bearing faults quickly and with high precision.

Comparison of passive and active power filters in oil drilling rigs

The Power systems in oil rigs are isolated, standalone systems. The source impedance is low. Bulk of the load is fluctuating nonlinear one and are powered directly from the generators without any series impedance. This paper investigates the harmonic problems, and presents the design of passive and active filter for oil rig power systems. The performance of the system with passive and active filters is studied. The simulation results and actual filed data are presented. Passive filter and active filters are compared in terms of THD improvement, filtering action, VAR rating, size, cost, efficiency and reliability. It is observed that passive filters are suitable where a space constraint is not an issue. In most of the rigs space is a constraint and active filter is the better choice.

Mitigation of magnetic fields near transmission lines using a passive loop conductor

This paper presents a concept to reduce the magnetic field generated from ac overhead transmission lines. The mitigation of magnetic field is performed by using a passive loop conductor. The method is applied to a flat transmission line configuration which is used frequently in extra high-voltage system. The effect of width and height of passive loop conductor on the reduction of magnetic field intensity is further investigated. Numerical results of magnetic field mitigation and further discussion are provided.

Human motion energy harvesting by design of handheld Linear PM Synchronous Generator

The authors present the experimental procedure to build a high power handheld-sized Linear Permanent Magnet Synchronous Generator (LPM SG) powered by human motion. This generator produces maximum energy for charging small-scale electronic devices such as mobile phones. Two different generator prototypes are compared through the presented theoretical and experimental results based on parallel charging of two 5V Li-ion battery supplies. The systematic choice of rare earth material PM, magnetization direction, solenoid wire diameter and number of turns have increased the generated power by more than tenfold between the discussed prototypes.

On the Suitability of Modeling Approaches for Power Electronic Converters

This paper is a summary and in-depth evaluation of some popular models for power electronic converters. Proper examples are given to support the evaluation with a complete comparison between the actual and simulated results. The paper presents the effort behind the design and analysis of DC-DC Converters and discuses their features. Some advantages and disadvantages of the studied models and the complicities of the nonlinear structure of the switching function of the converters are explained here. The paper offers some useful criteria for future selection and development on different DC-DC Converters

Static eccentricity fault detection in induction motors using wavelet packet decomposition and Gyration radius

In this paper, Finite Element Magnetic (FEM) method is used for modeling an induction motor before and after rotor eccentricity fault. The imbalance magnetic field in the air gap affects the stator current sinusoidal shape. The wavelet packet decomposition and Gyration radius methods are applied on the distorted current and torque signals respectively for the fault detection. The results of the two methods are consistent and reliable in eccentricity fault detection. In addition, by increasing the eccentricity severity for the static fault, both the energy of nodes and radius of phase space diagram of FEM diagram increase. Hence these two indices were used to measure the severity degrees of the static eccentricity fault.

Linear approach to performance assessment of a 4-tank system

Monitoring performance of nonlinear system is an important task for many real world applications especially for industrial environments; however its realization is usually difficult. Many monitoring performance methods have been introduced but they all rely on loops with accurate linear models of the system. The methods that are capable to identify MIMO nonlinear systems are scarce and linear models are not so accurate in modeling nonlinear systems. In this paper, a combined series of two capable algorithms is used for identifying the system. The method also identifies the existence of any possible disturbance simultaneously in order to reduce the complexity of the model. The logic behind the proposed methods is to utilize and approximate the interaction between the loops in the system. Moreover, the model explicitly defines the relationship between the outputs and the inputs of the system such that the performance indexes can be easily calculated. A well designed control strategy is introduced to determine the optimal values that are required to calculate the performance index. A reasonable decision on optimal situation is necessary to evaluate the performance index of a system. A minimum variances control strategy (MVC) is considered here as the most suitable feedback controller because it achieves the smallest possible closed-loop output variance. The above method has been utilized for a 4-tank system and then a minimum variance controller is designed for determining the optimal output such that the minimum possible variance, and consequently the performance index have been calculated. The achieved Index can be then used for monitor performance of systems in online and offline applications.