@article{UrschelBold2023, author = {Urschel, Sven and Bold, Sebastian}, title = {A Knowledge Discovery Process Extended to Experimental Data for the Identification of Motor Misalignment Patterns}, volume = {2023}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/machines11080827}, pages = {22}, year = {2023}, abstract = {The diagnosis of misalignment plays a crucial role in the area of maintenance and repair since misalignment can lead to expensive downtime. To address this issue, several solutions have been developed, and both offline and online approaches are available. However, online strategies using a small number of sensors show a higher false positive rate than other approaches. The problem is a lack of knowledge regarding the interrelations of a fault, disturbances during the diagnosis process, and capable features and feature vectors. Knowledge discovery in database is a framework that allows extracting the missing knowledge. For technical systems, optimal results were achieved by aligning (partially) automated experiments with a data mining strategy, in this case classification. The results yield a greater understanding of the interrelations regarding parallel misalignment, i.e., feature vectors that show good results also with varying load and realistic fault levels. Moreover, the test data confirm a specificity (range 0 to 1) for classification between 0.87 and 1 with the found feature vectors. For angular misalignment, potential vectors were identified, but these need further validation with a modified experiment in future work. For the study, two induction motors with 1.1 kW and 7.5 kW were considered. Furthermore, the findings were compared with additional motors of the same rated power. The findings of this work can help to improve the implementation of sensorless diagnostics on machines and advance the research in this field.}, language = {en} } @article{BeckerSchwammUrschel, author = {Becker, Vincent and Schwamm, Thilo and Urschel, Sven}, title = {Fault Investigation of Circulation Pumps to Detect Impeller Clogging}, volume = {10}, doi = {10.3390/app10217550}, pages = {1 -- 13}, abstract = {Pumps have a wide range of applicat ions. Methods for fault detection of motors are increasingly being used for pumps. In the context of this paper, a test bench is built to investigate circulation pumps for faults. As a use case, the fault of impeller clogging was first measured and then examined with the help of motor current signature analysis. It can be seen that there are four frequencies at which there is an increase in amplitude in case of a fault. The sidebands around the supply frequency are in particular f ocus. The clogging of three and four of a total of seven channels leads to the highest amplitudes at the fault frequencies. The efficiency is reduced by 9 to 15\% in case of faulty opera tion. These results indicate that the implementation of fault detection algorithms on the pump electronics represents added value for the pump operator. Furthermore, the results can be transferred to other applications.}, language = {en} }