Himanshu Jain

Graduation Semester and Year




Document Type


Degree Name

Master of Science in Electrical Engineering


Electrical Engineering

First Advisor

Wei-Jen Lee


As world economy continues to globalize, increased competition has lead to a reduction in profit margins. To remain competitive in such an economy, an industry needs to reduce the downtime of critical components even for maintenance purposes. However, this should not jeopardize the genuine maintenance needs of the equipments. In other words, an optimized maintenance strategy is needed, in which maintenance is performed only when a need for it arises; unlike the currently popular periodic maintenance. Condition based Maintenance (CBM) is one such strategy. Induction machines are work horse of an industry. Their criticality to industry may be gauged by the fact that they account for more than 60% percent of the energy consumed in USA's manufacturing sector. Hence, CBM for induction machines makes perfect economic sense. It is for this reason that literature is abounds with techniques for detecting various fault conditions at an incipient stage in induction machines. Rotor imbalance is one such fault condition. Several papers have identified the signatures to look for in stator current of induction machines for detecting rotor imbalance. However, an accurate explanation for appearance of these signatures is lacking. Moreover, only one phase of stator current has been used for detecting and classifying the severity of rotor imbalance. Since current sensors are available in all the three phases for protection and control purposes, combining fault information from all of them may yield more accurate results. This thesis, therefore, attempts to address the above deficiencies in existing rotor imbalance fault diagnostics techniques.


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington