Abstract:
Condition monitoring of pumps is very important due to their critical role in industrial plants. This thesis focuses on vibration based fault detection and diagnosis of the exible impeller pumps which are mostly used in pharmaceutical, food processing and textile nishing plants. The common problems of this type of positive displacement pumps are associated with the impellers and the cavitation is the main cause of impeller wear and damage. In the literature, there are many studies focused on the condition monitoring of positive displacement pumps. Nevertheless, none of these studies is particularly concentrated on the exible impeller pumps. In this study, rstly, the data acquisition system has been devised for collecting vibration and motor current signals from the experimental setup. The separation ability of the features extracted from the vibration and motor current signals collected under di erent pump conditions has been investigated. The analysis regarding motor current signals has shown that they are not that distinctive to separate the conditions experienced by exible impeller pumps. The classi ers exploiting features based on the time domain, frequency domain and time-frequency domain representations of vibration signals have been trained. The results regarding the performances of trained multi-class support vector machine and feedforward neural network classi ers have been presented as well. The ndings of the thesis show that the feedforward neural networks exploiting wavelet variance features perform very well for classifying the exible impeller pump conditions focused on. Eventually, a digital signal processing chain for fault detection and diagnosis of exible impeller pumps has been proposed and realized in the embedded hardware.
