Vibration based condition monitoring of pumping systems in textile dyehouses

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 flexible impeller pumps which are mostly used in pharmaceutical, food processing and textile finishing 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 flexible impeller pumps. In this study, firstly, 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 different 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 flexible impeller pumps. The classifiers 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 classifiers have been presented as well. The findings of the thesis show that the feedforward neural networks exploiting wavelet variance features perform very well for classifying the flexible impeller pump conditions focused on. Eventually, a digital signal processing chain for fault detection and diagnosis of flexible impeller pumps has been proposed and realized in the embedded hardware.