Rotor Faults In Induction Machines Research Articles

Advanced Doubly Fed Induction Machine Rotor Fault Diagnosis Based on Wavelet Analysis in Closed-Loop Operation Under Time–varying Condition

This paper introduces a new diagnostic technique for the detection of incipient electrical rotor faults in doubly fed induction Machine (DFIM) for wind power systems. In the considered application, the rotor is supplied by a static converter for the control of active and reactive power flows from the generator to the electrical grid. A new diagnostic method based on the rotor modulating signals pre-processing by Frequency Sliding (FS) and Discrete Wavelet Transform (DWT) thereby is here proposed to detect rotor faults dynamically over time. Experimental and simulation results demonstrate the effectiveness of the proposed approach under time-varying conditions.

Fast Real-Time RDFT- and GDFT-Based Direct Fault Diagnosis of Induction Motor Drive

This paper presents the theoretical analysis and experimental verification of a direct fault harmonic identification approach in a converter-fed electric drive for automated diagnosis purposes. On the basis of the analytical model of the proposed real-time direct fault diagnosis, the fault-related harmonic component is calculated using recursive DFT (RDFT) and Goertzel DFT (GDFT), applied instead of the full spectrum calculations required in the most popular FFT algorithm. The simulation model of an inverter sensorlessly controlled induction motor drive is linked with the induction machine rotor fault model for testing the sensitivity of the GDFT- and RDFT-based fault diagnosis to state variable estimation errors. According to the presented simulation results, the accuracy of the direct identification of a fault-related harmonic is sensitive to the quality of fault harmonic frequency estimation. The sensitivity analysis with respect to RDFT and GDFT algorithms is included. Based on the experimental setup with a sensorlessly controlled induction motor drive with the investigated rotor fault, fault diagnosis algorithms were implemented in the microprocessor by integration with the control system in one microcontroller and experimentally verified. The RDFT and GDFT approach has shown accurate and fast direct automated fault identification at a significantly decreased number of arithmetical operations in the microcontroller, which is convenient for the frequency-domain fault diagnosis in electric drives and supports fault-tolerant control system implementation.

A Novel Experimental Method to Detect Early Rotor Faults in Induction Machines

The Induction Machines (IMs) have numerous industrial interests, as they have an uncomplicated design, strong, robust, and vulnerable to maintain. Despite that fact, a small fault in one rotor bar or a low Unbalance Rotor (UR) causes the break of this bar. Subsequently, the break of the adjacent bar because of the mechanical vibration generated by this imbalance of the rotor. This contribution proposes a new method based on the analysis of the stator line current to enhance the reliability of the monitoring system of an incipient defect in one rotor bar of the Induction Motor (IM). This new method, which combines the Electrical-Time-Synchronous-Averaging (ETSA) procedure with the Vector Park Transform (VPT) technique and Motor Current Signature Analysis (MCSA), and a Fuzzy Logic Algorithm (FLA), is performed. Indeed, an incipient rotor failure cannot be detected using the stator line current signal curve. Absolutely, in cases of the minor defect in one only rotor bar and low UR at low load. Nevertheless, the residual energy contained in each spectrum is considered as input variables for a fuzzy logic system to classify the type and the level of rotor defects. The proposed algorithm applied to the measured stator line-current has been built under the MatLab environment. The experimental obtained results confirm that the conceived algorithm can classify exactly the rotor faults.

Temporal envelope detection by the square root of the three-phase currents for IM rotor fault diagnosis

This paper deals with a reliable and effective method for induction machine rotor fault diagnosis, using three-phase stator currents. Through a theoretical demonstration based on the magnetic field approach, it has been shown that broken rotor bars produce amplitude modulation in the stator current which can be extracted with very low complexity by calculating the root of the squared three-phase stator currents. The theoretical background of the proposed method is presented then experimentally confirmed by using three currents measured from a test bench that contains three motors, one healthy and two other with broken bars. Each motor is subjected to two load conditions (low and high). The obtained results show the robustness and effectiveness of the proposed technique, even at low-load conditions, comparing to the classical MCSA method.

Effects of the Speed Loop on the Diagnosis of Rotor Faults in Induction Machines

The development of high-performance static converters and advanced control techniques has led to a widespread diffusion of asynchronous drive systems in industrial plants. One of the strong points of the induction machines is their reliability; however, an unexpected failure may still result in a considerable economic loss. This fact has stimulated a prolific research activity concerning the continuous monitoring of the wear status of induction machines. The knowledge of their condition allows implementing condition-based maintenance techniques, which have proven to be very effective in critical processes. In previous papers, the authors have focused their attention on the detection of broken rotor bars in inverter-fed induction machines. Two indexes have been defined: Both of them are simple methods to estimate the same fault indicator, namely, the ratio between the amplitude of the power oscillation and the average power draw of the drive system. However, this fault indicator exhibits a noticeable dependence on the mechanical load as well as on the speed loop parameters. In this paper, a deep investigation about these dependences is provided. The study suggests introducing a new fault indicator which is much less sensitive with respect to the torque developed by the motor. Furthermore, the analysis allows predicting how the speed loop affects the fault detection techniques. The results of the study and the effectiveness of the new fault indicator have been first verified by means of numerical simulations and then validated through an experimental activity.

Induction machine current space vector features to effectively discern and quantify rotor faults and external torque ripple

Intensive research efforts have been spent with the aim of separating the effects of load torque unbalance from those of rotor faults in induction machines. Recent literature reports that active and reactive components of the current space vector seem to show different features in correspondence of the two fault events. This study shows the angular displacement of the above components is fundamental in order to distinguish external torque ripple from rotor faults. The fault severity and the external torque ripple can also be effectively determined. Moreover, the proposed technique allows to assess the range of validity of the usual motor current signature analysis (MCSA), based on the sum of the amplitude of stator current sidebands, for quantifying rotor bar faults.

Analytical Approach of the Stator Current Frequency Harmonics Computation for Detection of Induction Machine Rotor Faults

The aim of this paper is to analyze theoretically and experimentally the stator current of a three-phase squirrel-cage induction machine in order to show how it is influenced by electrical rotor faults. The approach used for this study analyzes the modification introduced by n broken rotor bars in the rotor cage magnetomotive force and then estimates the resulting frequency spectrum in the stator current. This approach is validated in a 3-kW 230-V/400-V 50-Hz 2850-r/min two-pole three-phase induction machine, showing the sensitive frequency components to rotor fault condition.

Simulation and fault detection of three-phase induction motors

Computer simulation of electric motor operation is particularly useful for gaining an insight into their dynamic behaviour and electro-mechanical interaction. A suitable model enables motor faults to be simulated and the change in corresponding parameters to be predicted without physical experimentation. This paper presents both a theoretical and experimental analysis of asymmetric stator and rotor faults in induction machines. A three-phase induction motor was simulated and operated under normal healthy operation, with one broken rotor bar and with voltage imbalances between phases of supply. The results illustrate good agreement between both simulated and experimental results.

Detection of rotor faults in squirrel-cage induction machines at standstill for batch tests by means of the Vienna monitoring method

This paper addresses the possibility of integration of an induction machine rotor fault monitoring technique into batch tests. Conventional monitoring techniques are designed for operating conditions near nominal speed and load. Generally, load torque is not available at batch test as the test sample should not or cannot be clutched. However, at standstill, shaft torque and rotor currents are big enough to enable rotor monitoring. Therefore, short-circuit tests are applicable for diagnostic purposes. This application is demonstrated by means of the Vienna Monitoring Method, which is a model-based torque evaluation technique.

Neural networks aided on-line diagnostics of induction motor rotor faults

An improvement of induction-machine rotor fault diagnosis based on a neural network approach is presented. A neural network can replace in a more effective way the faulted machine models used to formalize the knowledge base of the diagnostic system with suitably chosen inputs and outputs. Training the neural network by data obtained from experimental tests on healthy machines and from simulation in the case of faulted machines, the diagnostic system can discern between healthy and faulty machines. This procedure replaces the formulation of a trigger threshold, required in the diagnostic procedure based on the machine models. >