| dc.description.abstract | Analysis of electrical signatures has been in use for some time to assess the
condition of induction motors. In most applications, induction motors are used to
drive dynamic loads, such as pumps, fans, and blowers, by means of belts, couplers and
gear-boxes. Failure of either the electric motors or the driven loads is associated with
operational disruptions. The large costs associated with the resulting idle equipment
and personnel can often be avoided if the degradation is detected in its early stages,
prior to reaching catastrophic failure conditions. Hence the need arises for cost-
effective schemes to assess not only the condition of the motor but also of the driven
load.
This work presents an experimentally demonstrated sensorless approach for model-
based detection of three different classes of faults that frequently occur in centrifugal
pumps. A fault isolation scheme is also developed to distinguish between motor re-
lated and pump related faults. The proposed approach is sensorless, in the sense that
no mechanical sensors are required on either the pump or the motor driving the pump.
Rather, fault detection and isolation is carried out using only the line voltages and
phase currents of the electric motor driving the pump, as measured through standard
potential transformers (PT's) and current transformers (CT's) found in industrial
switchgear.
The developed fault detection and isolation scheme is insensitive to electric power
supply variations. Furthermore, it does not require a priori knowledge of a motor or pump model or any detailed motor or pump design parameters; a model of the system
is adaptively estimated on-line. The developed algorithms have been tested on three
types of staged pump faults using data collected from a centrifugal pump connected
to a 3, 3 hp induction motor. Results from these experiments indicate that the
proposed model-based detection scheme effectively detects all staged faults with fault
detection times comparable to those obtained from vibration analysis. In addition to
the staged fault experiments, extended healthy operation reveals no false alarms by
the proposed detection algorithm. The proposed fault isolation method successfully
classifies faults in the motor and the pump without any mis-classification. | en |
