Detection of Static Air-Gap Eccentricity in Three-Phase Squirrel Cage Induction Motor Through Stator Current and Vibration Analysis

To read the full-text of this research, you can request a copy directly from the authors.


Three-phase squirrel cage induction motor being a core component of industrial drives needs fault detection strategies which can detect internal faults in very early stage of its development. This can result in enormous financial saving in industries. Simulation studies with suitable mathematical models helps in identification of fault signatures in the diagnostic signal. The work presented in this paper addresses the issue of detection of incipient static eccentricity faults. Modelling of motor with static eccentricity fault is done and characteristic signatures were identified in frequency spectrum of stator current. These components were also identified in the vibration spectrum, by conducting a practical experimentation in three-phase squirrel cage induction motor with fabricated static eccentricity. The results validates the modelling approach and also demonstrates the suitability of vibration and stator current signal for the diagnosis of incipient static eccentricity faults.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... It indicated that the voltage spectrum of the neutral point was more sensitive to the air-gap static eccentricity. Bindu and Thomas (2018) proved the applicability of the vibration signal and stator current signal in diagnosing static eccentricity through experiments on three-phase squirrel-cage asynchronous motors with static eccentricity. Ding et al. (2015) studied the influence of different air-gap static eccentricity on magnetic field strength and core loss based on electromagnetic theory and the finite-element method. ...
Full-text available
The air-gap state between the stator and rotor is an important indicator to measure the performance of a motorized spindle. It affects the temperature field distribution of the motorized spindle and the machining accuracy of the mechanical parts. Since the accurate thermal model is the basis of the research on the temperature field distribution of the motorized spindle, in this paper, firstly, the mechanical loss, electrical loss and magnetic loss of the motor under different air-gap eccentricities are calculated and the heat-generating power of an angular-contact ball bearing is obtained based on Harries contact theory. Secondly, the thermal model of the motorized spindle is established and the steady-state temperature field of the motorized spindle is simulated by using ANSYS, and the influence of air-gap eccentricity on the temperature field of the motorized spindle is discussed. Finally, the circumferential temperature field distribution of the motorized spindle with the air-gap eccentricity is verified by experiment. The results show that the air-gap eccentricity has a significant influence on the non-uniform temperature field of the motorized spindle.
Full-text available
Over the years, induction machines (IMs) have become key components in industry applications as mechanical power sources (working as motors) as well as electrical power sources (working as generators). Unexpected breakdowns in these components can lead to unscheduled down time and consequently to large economic losses. As breakdown of IMs for failure study is not economically feasible, several IM computer models under faulty conditions have been developed to investigate the characteristics of faulty machines and have allowed reducing the number of destructive tests. This paper provides a review of the available techniques for faulty IMs modelling. These models can be categorised as models based on electrical circuits, on magnetic circuits, models based on numerical methods and the recently proposed in the technical literature hybrid models or models based on finite element method (FEM) analytical techniques. A general description of each type of model is given with its main benefits and drawbacks in terms of accuracy, running times and ability to reproduce a given fault.
Full-text available
There are many limitations to applying online spectrum analysis techniques for diagnosis of closed-loop inverter-fed induction motors due to variable load or frequency operation, and the masking effect of the current regulator. In this paper, a new automated approach for testing inverter-fed induction machines for airgap eccentricity is proposed. The main concept is to use the inverter to excite the machine with a pulsating field at multiple angular positions to observe the variation of equivalent impedance due to eccentricity, whenever the motor is stopped. It is shown that the increase in the value of the equivalent (leakage) inductance under standstill excitation can be used as an indicator of increasing airgap eccentricity. Standstill testing can provide reliable assessment of eccentricity that is independent of variations in operating conditions, load interferences, or motor type. An experimental study on a 7.5-hp induction motor verifies that eccentricity can be detected with high sensitivity and reliability without additional hardware.
Full-text available
This paper discusses use of air-gap torque spectra as a means of identifying faults in cage rotors. Being dependent on both stator and rotor currents, the torque is very sensitive to faults in the rotor. Through a comparative study using a detailed machine model and the standard dq model, the paper shows that the characteristic frequencies generated by a particular fault are preserved even if the standard dq model is used for estimation of air-gap torque. This is validated through a practical hardware implementation for online spectrum estimation of air-gap torque using TMS320C31, where several faulted cage rotors were used for study.
Conference Paper
Three phase squirrel cage induction motors are the most popular motors in industries. Electrical, magnetic, mechanical, thermal and environmental stresses during operating conditions lead to internal faults in it. There are no reliable non-invasive tools available for early diagnoses of internal faults. Hence, these internal faults are likely to be left undetected in its early stage, leading to unscheduled maintenance, process shutdown and huge financial loss in industries. Early detection of faults helps to save resources by avoiding process shutdown/repair of machines. Hence, there is a need for a reliable non-invasive condition monitoring system for three phase squirrel cage induction motors. Condition monitoring involves non-invasive acquisition of signals, processing, fault signature extraction, decision making on the presence and type of faults. This paper reviews the current trends in internal fault diagnosis of induction machines and identifies future research options. A statistical analysis of the results of motor current signatures obtained from a motor with rotor bar faults is done to study the relative effect of fault severity and load variation on the current signature.
Medium-voltage (MV) induction motors are widely used in the industry and are essential to industrial processes. The breakdown of these MV motors not only leads to high repair expenses but also causes extraordinary financial losses due to unexpected downtime. To provide reliable condition monitoring and protection for MV motors, this paper presents a comprehensive survey of the existing condition monitoring and protection methods in the following five areas: thermal protection and temperature estimation, stator insulation monitoring and fault detection, bearing fault detection, broken rotor bar/end-ring detection, and air gap eccentricity detection. For each category, the related features of MV motors are discussed; the effectiveness of the existing methods are discussed in terms of their robustness, accuracy, and implementation complexity. Recommendations for the future research in these areas are also presented.
A new method for the detection of rotor eccentricity faults in a closed-loop drive-connected induction motor is reported in this paper. Unlike a line-fed electric motor, the eccentricity-related fault signals exist in the current as well as the voltage of a drive-connected motor. Meanwhile, since the speed and therefore the mechanical load can change widely in variable speed applications, the amplitudes of the fault signals will vary accordingly. An artificial neural network is used in the detection to learn the complex relationship between the eccentricity-related harmonic amplitudes and the operating conditions. The neural network can estimate a threshold corresponding to an operating condition, which can then be used to predict the motor condition. The neural network is trained and tested with data collected on drive-connected 4-pole, 7.5 Hp, three-phase induction motors. The experimental results validate that the detection method is feasible over the whole range of operating conditions of the experimental motors.
A new multiple coupled circuit model is presented for simulation of induction machines with both arbitrary winding layout and/or unbalanced operating conditions. The model is derived by means of winding functions. No symmetry is assumed. The parameters of the model are calculated directly from the geometry and winding layout of the machine. The behavior of an induction machine during starting is simulated using this model. The results are shown to be in good agreement with the solution obtained by a conventional d-q model for symmetric conditions. The new model is then extended to the solution of a wide variety of fault conditions such as broken bars and end rings and open or short circuited motor coils
Non-invasive techniques for fault diagnoses of induction machines
  • V V Thomas
  • S Bindu
Mathematical modelling of dynamic induction motor and performance analysis with bearing fault
  • V A Kamal
  • K Giri
  • VA Kamal
Electric Motor Drives: Modelling
  • R Krishnan
Detection of bearing fault in three phase induction motor using wavelet
  • A Kamal
  • V K Giri