On-line condition monitoring and detection of stator and rotor faults in induction motors.

Abstract

Induction motors are reliable and widely used in industrialised nations. However induction motors, like any other machine, will eventually fail. If the failure is not anticipated, it can result in a significant revenue loss. Therefore, there is a strong need to develop an efficient maintenance program. The most cost-effective solution is condition-based maintenance. An effective condition-based maintenance program requires an on-line condition monitoring system that can diagnose the condition of an induction motor in order to determine the types of faults and their severity while the motor is under a normal operating condition. The work in this thesis investigates the detection of stator and rotor faults (i.e. shorted turn faults, eccentricity faults, and broken rotor bar faults) using three types of sensor signals (i.e. current, leakage flux, and vibration) under different loading conditions. The work is based on an extensive series of sensor measurements taken using a number of nominally identical healthy machines (2.2 kW) and custom-modified machines (2.2 kW) with configurable stator and rotor fault settings. The thesis starts by investigating the estimation of rotor speed and rotor slot number. These two parameters are important in determining the fault frequency components that are used for detecting the stator and rotor faults. The rotor speed investigation compares four different estimation methods from the three different sensor signal types. It is found that the speed estimation techniques based on the eccentricity harmonics and the rotor frequency in the stator current, the axial leakage flux, and the motor vibration sensor signals can detect the rotor speed very accurately even when the load is as low as 2%. Similarly, this thesis proposes three different rotor slot number estimation techniques from the three different types of sensors and demonstrates that all three techniques can estimate the rotor slot number accurately. In addition, it is shown that the reliability of the estimation techniques can be increased significantly when the three techniques are combined. The shorted turn investigation in this thesis examines and compares potential shorted turn features in the three sensor signal types under five different fault severities and ten different loading conditions. The useful shorted turn features are identified in the thesis, and then examined against variations between the healthy machines in order to determine the loads and the fault severities in which the feature can reliably detect the faults. The results show that the feature based on the EPVA (extended Park’s vector approach) is the best method. This feature can detect turn to turn faults with a severity of 3.5% or greater at loads greater than 20% and phase to phase turn faults with a severity of 1.7% or greater under all loading conditions. However, estimating the fault severity is generally found to be difficult. The thesis also examines the feasibility of detecting static eccentricity faults using the different types of sensor signals under ten different loading conditions. The thesis compares potential eccentricity features under nine different fault severities. The useful features are identified and then combined through weighted linear combination (WLC) in order to produce a better eccentricity fault indicator. The indicator begins to show significant magnitude variation when the fault severity is greater than or equal to 25% and the load is greater than or equal to 25%. The experimental results show that detecting the static eccentricity faults is possible but estimating the fault severity may be difficult. Furthermore, the effects of misalignment faults on the useful eccentricity features are investigated. In this thesis, the analysis of broken rotor bar faults is performed under motor starting and rundown operation. The starting analysis introduces a new approach to detect broken rotor bar faults that utilises the wavelet transform of the envelope of the starting current waveform. The results of the wavelet transform are then processed in order to develop a normalised parameter, called the wavelet indicator. It is found that the wavelet indicator can detect a single broken bar under all loading conditions during motor starting operation. The indicator also increases its magnitude as the severity of the fault increases. On the other hand, the rundown analysis proposes several broken rotor bar fault detection techniques which utilise the induced voltage in the stator windings and the stator magnetic flux linkage after supply disconnection. The experimental results show that detecting the faults during rundown is generally difficult. However, the wavelet approach, which is based on monitoring changes in the motor torque for a given slip, seems to give the best result.