Sensorless Detection and Isolation of Faults in Motor-Pump Systems Available to Purchase

Induction motors are the workhorses of industry and a lot of effort has been invested in detecting and diagnosing induction motor faults through the analysis of the motor electrical signals. However, in many industrial applications, electric motors are used to drive dynamic loads such as pumps, fans, blowers etc. Failure of either the motors or the driven loads is associated with operational disruption. Consequently it would be beneficial if the entire motor-pump system is monitored and diagnosed. The large costs associated with production losses can be avoided if system degradation can be detected at early stages prior to failure. Moreover, downtime can be further reduced if the faulty component within the drive power system can be isolated thereby aiding plant personnel to be better prepared with spares and repair kits. Hence there is not only a strong need for cost-effective detection schemes to assess the condition of the drive power system as a whole, but also a strong need for efficient isolation schemes to identify the component within the system that is faulty. This paper describes a sensorless approach to detect and isolate induction motor and/or centrifugal pump faults. Motor and pump bearing degradation is considered to validate the performance effectiveness of the proposed scheme. No add-on sensors, on either the motor or the pump, are used in the development of the proposed method to avoid any reduction in overall system reliability and prevent increased costs. In fact, motor and/or pump bearing degradation is detected and isolated using only the motor line voltages and phase currents. The proposed technique is insensitive to electric power supply fluctuations and mechanical load variations and it does not require prior knowledge of either the motor or the pump design parameters. Hence this approach can be easily ported to motor-pump systems of varying manufacturers and sizes. The developed algorithm has been tested on accelerated fault data collected from a centrifugal pump fluid loop driven by a 3-φ, 3 hp induction motor. Results from these experiments indicate that the proposed fault detection and isolation scheme successfully detects and classifies bearing degradation in the motor and/or the pump without false positives or misclassification.