Browsing by Subject "DDC::600 Technology (Applied sciences)::620 Engineering & allied operation"
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    Multivariate Sensor Dataset of an Industrial Rotogravure Printing Press (MSDIRPP)
    (University of Wuppertal, 2022-05-31) Tobias Enk
    We present a multivariate sensor dataset for machine learning research in context of industrial print application. The dataset contains 7608 rolls of pre-processed multivariate sensor data of a single production scale rotogravure printing press. The data volume corresponds to 43.181 km of printed paperboard and paper. For each roll we provide high-resolution sampled inline sensor data, machine condition labels and several meta information. Besides basic information like machine speed the dataset contains web movement data such as web edge and web tension measurements, material measurement like web moisture and print quality data such as register measurements in cross and machine direction for 11 print units. We publish the dataset to provide data researchers a strong baseline dataset for several applications in industrial printing.
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    The Concept of Failure-Cause-Searching and Solution-Finding Algorithm (FusLa)
    (University of Wuppertal, 2021-12-01) Ansari, A. ; Schlüter, N. ; Heinrichsmeyer, M.
    The solution proposed in this fundamental research is a Failure-Cause-Searching and Solution-Finding Algorithm (FusLa), which should enable the identification and elimination of failure causes in the production system based on complaint information from the use phase of a product system. To realize this, a four-phase algorithm was designed. In the first phase, automated information probing is performed, which filters out the relevant information from the complaints and prepares it for further processing. In the second phase, the FusLa then automatically determines a priority value for each complaint based on the probed information. Based on the prioritization, the algorithm is able to localize potential causes for the identified failures in the third phase. Based on the localized failure causes, the algorithm then derives suitable measures.