Development of a CBM based service indicator for UFD replacements - An introductory study

Abstract: Dialysis is a life-sustaining treatment for many people around the world. In or- der to meet the high demands on the dialysis machines, replaceable parts must be exchanged in proper time. Condition based maintenance (CBM) bases its service decisions on the actual health status of the component. The goal of this master’s thesis was to develop an algorithm based on machine learning, constituting the first step of a CBM based service indicator monitoring the dialysis ultra filter (UFD) of Baxter’s AK 98 dialysis machine. Real treatment data retrieved from ten dialysis machines have been an- alyzed. Signals believed to be relevant to the UFD status were preprocessed and analyzed. From them different features were extracted whereof some were found in CBM related literature. Two different feature selection methods were used to select 10 out of the 178 available features. Different labeling meth- ods were tested and evaluated together with other relevant parameters in the algorithm. The final algorithm used a k-nearest neighbors (kNN) classifier with k = 12. The classification accuracy was approximately twice as good as a ran- dom guess. The main reason for not achieving a better result was that only six features appeared to contain relevant information regarding the UFD status. Furthermore, these features were derived from the same signal and closely related. Despite this, the developed algorithm did show promising result in detecting the UFD degradation level but further development will be needed. The main focus should be to improve the signal quality and/or find more relevant signals and/or features.