Use this URL to cite or link to this record in EThOS:
Title: Current based condition monitoring of electromechanical systems : model-free drive system current monitoring : faults detection and diagnosis through statistical features extraction and support vector machines classification
Author: Bin Hasan, M. M. A.
ISNI:       0000 0004 2747 8979
Awarding Body: University of Bradford
Current Institution: University of Bradford
Date of Award: 2012
Availability of Full Text:
Access from EThOS:
Access from Institution:
A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severity
Supervisor: Ebrahimi, Kambiz; Mujtaba, Iqbal M. Sponsor: Ministry of Higher Education, Libya ; Switchgear & Instruments Ltd
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Predictive maintenance ; Condition monitoring ; Induction motor ; Fault detection ; Machine learning ; Support vector machines ; Mechanical faults ; Equipment failure