@article{AssafoLangendoerfer2025, author = {Assafo, Maryam and Langend{\"o}rfer, Peter}, title = {Tool remaining useful life prediction using feature extraction and machine learning-based sensor fusion}, series = {Results in Engineering}, volume = {28}, journal = {Results in Engineering}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2590-1230}, doi = {10.1016/j.rineng.2025.107297}, year = {2025}, abstract = {Tool remaining useful life prediction (RUL) is a critical task for predictive maintenance in manufacturing. Common limitations of existing data-driven solutions include: 1) Dependence on tool wear labels which are intricate to obtain on shop floors. 2) High resource requirements, affecting applicability on resource-constrained Internet-of-things devices. 3) Heavy feature engineering. To address these limitations, we present a methodology aiming at accurately predicting RUL without using wear labels, while ensuring implementation efficiency and minimal feature engineering. It involves extracting time-domain features and multiscale features using maximal overlap discrete wavelet transform (MODWT) from three cutting-force sensor signals. Without undergoing any feature selection or dimensionality reduction, the features are fed to machine learning (ML) regression models where they are fused into an RUL decision. For this purpose, one-to-one and sequence-to-sequence regression using random forest (RF) and different long short-term memory (LSTM) networks were used, respectively. The 2010 PHM Data Challenge milling dataset was used for validation. The results highlighted the significant role of sensor fusion in reducing prediction errors and increasing the performance consistency over three test cutters, compared to single sensors. Global interpretations were provided using RF-based feature importance analysis. Our methodology was compared with six existing state-of-the-art works, including different end-to-end deep learning (DL) models using raw data as input, and works coupling heavy feature engineering with DL. The results showed that our methodology consistently outperformed all the comparative methods over the test cutters, despite using fewer sensors, which further proves its competitiveness and suitability in resource- and sensor-constrained environments.}, subject = {Cutting tool; Data-driven models; Feature extraction; Machine learning; Predictive maintenance}, language = {en} } @phdthesis{Tran2021, author = {Tran, Anh Duc}, title = {Konzeption einer Big-Data-Architektur zur Optimierung und Flexibilisierung der Automobilproduktion}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-56012}, school = {BTU Cottbus - Senftenberg}, year = {2021}, abstract = {Im Zeitalter der Digitalisierung steht die Automobilindustrie vor innerbetrieblichen Herausforderungen, welche sowohl die angewandten Technologien, als auch den Menschen gleichermaßen betreffen. Dabei gilt es die Effizienz im Produktionsprozess anhand vorhandener Daten zu maximieren, indem bestehende Ressourcen optimal eingesetzt werden. Die effiziente Nutzung vorhandener Ressourcen ist ein entscheidender Faktor zur Erreichung globaler Ziele, wie dem einer ressourcenschonenden, klimaneutralen Produktion. Das Ziel dieser Arbeit ist die Erarbeitung einer Big-Data-Architektur, mit der ein Ansatz des Predictive Maintenance in der Produktion zum Trage kommt. Das Big-Data-Konzept soll system{\"u}bergreifend die Datenflut in der Automobilproduktion abfangen und durch eine effiziente Aufarbeitung f{\"u}r die Verwertbarkeit sorgen. Hinsichtlich der Orchestrierung der Datensysteme liegt der Schwerpunkt in der Automatisierung der Datenanalyseprozesse. Dar{\"u}ber hinaus wird dem Leser das disruptive Potenzial von Big-Data-Technologien n{\"a}her gebracht.}, subject = {Digitalisierung; Produktion; Big Data; Datenanalyse; Predictive maintenance; Digitazation; Production; Big Data; Data analytics; Predictive maintenance; Kraftfahrzeugindustrie; Big Data}, language = {de} }