Žlabravec Veronika, Strbad Dejan, Dogan Anita, Lovric Mario, Janči Tibor, Vidaček Filipec Sanja
2022
Lovric Mario, Kern Roman, Fadljevic Leon, Gerdenitsch, Johann, Steck, Thomas, Peche, Ernst
2021
In industrial electro galvanizing lines, the performance of the dimensionally stable anodes (Ti +IrOx) is a crucial factor for product quality. Ageing of the anodes causes worsened zinc coatingdistribution on the steel strip and a significant increase in production costs due to a higher resistivityof the anodes. Up to now, the end of the anode lifetime has been detected by visual inspectionevery several weeks. The voltage of the rectifiers increases much earlier, indicating the deteriorationof anode performance. Therefore monitoring rectifier voltage has the potential for a prematuredetermination of the end of anode lifetime. Anode condition is only one of many parameters affectingthe rectifier voltage. In this work we employed machine learning to predict expected baseline rectifiervoltages for a variety of steel strips and operating conditions at an industrial electro galvanizingline. In the plating section the strip passes twelve “Gravitel” cells and zinc from the electrolyte isdeposited on the surface at high current densities. Data, collected on one exemplary rectifier unitequipped with two anodes, have been studied for a period of two years. The dataset consists of onetarget variable (rectifier voltage) and nine predictive variables describing electrolyte, current andsteel strip characteristics. For predictive modelling, we used selected Random Forest Regression.Training was conducted on intervals after the plating cell was equipped with new anodes. Our resultsshow a Normalized Root Mean Square Error of Prediction (NRMSEP) of 1.4 % for baseline rectifiervoltage during good anode condition. When anode condition was estimated as bad (by manualinspection), we observe a large distinctive deviation in regard to the predicted baseline voltage. Thegained information about the observed deviation can be used for early detection resp. classificationof anode ageing to recognize the onset of damage and reduce total operation cost
Lovric Mario, Stipaničev Draženka , Repec Siniša , Malev Olga , Klobučar Göran
2018
Lovric Mario, Krebs Sarah, Cemernek David, Kern Roman
2018
The use of big data technologies has a deep impact on today’s research (Tetko et al., 2016) and industry (Li et al., n.d.), but also on public health (Khoury and Ioannidis, 2014) and economy (Einav and Levin, 2014). These technologies are particularly important for manufacturing sites, where complex processes are coupled with large amounts of data, for example in chemical and steel industry. This data originates from sensors, processes. and quality-testing. Typical application of these technologies is related to predictive maintenance and optimisation of production processes. Media makes the term “big data” a hot buzzword without going to deep into the topic. We noted a lack in user’s understanding of the technologies and techniques behind it, making the application of such technologies challenging. In practice the data is often unstructured (Gandomi and Haider, 2015) and a lot of resources are devoted to cleaning and preparation, but also to understanding causalities and relevance among features. The latter one requires domain knowledge, making big data projects not only challenging from a technical perspective, but also from a communication perspective. Therefore, there is a need to rethink the big data concept among researchers and manufacturing experts including topics like data quality, knowledge exchange and technology required. The scope of this presentation is to present the main pitfalls in applying big data technologies amongst users from industry, explain scaling principles in big data projects, and demonstrate common challenges in an industrial big data project