## 4 Data Description  
Real-world use-cases are often subjected to unforeseen changes, and our welding use-case is no different.  
Our model distinguishes itself from state-of-the-art statistical models in its ability to adapt to changes in the underlying behavior. These kinds of changes are typical to real-world use-cases, but the literature discussing it is limited. One work notes behavioral changes throughout time for measured temperatures and attributes them to changing physical and chemical properties of the steel but does not explicitly take these into account for evaluation [11].  
Our data originates from the welding machine used in the continuous annealing and galvanization line of AreclorMittal Ghent and spans a period from March 2019 to August 2020. For each weld, a record was made of the metadata of both coils, setup welding parameters, used welding parameters and welding measurements. Metadata included the thickness, width, material type and the identifier of both coils, as well as a timestamp. Setup parameters comprise all welding machine settings as defined by the selected welding program, including weld speed, machine voltage, welding pressure, planning pressure and settings for the overlap of both coils. The used parameters are the settings effectively used for the welding process; these match the setup values unless the operator has intervened. The measured values are collected during the welding process and include values such as the actual weld speed, pressures, current and temperature. These features were sampled at 50 Hz throughout the weld, and statistical features, such as average and standard deviation, were stored per weld.  
As the data collection process is fully automated, weld tests and welds made during maintenance periods are also included. Unfortunately, due to the way the data collection worked, the records for these welds turned out to be unreliable due to a software flaw where data of new coils were loaded before rewelding was completed. As there was no feature available that described the type of weld (i.e., normal, reveld or weld tests), we used various sanity checks and data originating from other parts of the production line to filter out welds where records may have been incorrect. For ease of evaluation in later experiments, we also removed any welding programs where fewer than 10 welds were present in the data set. After filtering, we obtained records for 19,910 welds, comprising over 111 different welding programs.  
Figure 1 shows the median measured welding current for all welds made using one specific welding program, meaning all welds were made on plates with a similar type of steel and thickness. Each dot in this figure represents a single weld. While we see slight changes in the current over time that might be explained due to slight changes in the chemical properties of the steel over time, two large jumps stand out. The first jump (September 2019) corresponds with maintenance of the welding machine, where a copper conductor was replaced. The second jump (March 2020) corresponds to another maintenance period, though no parts were replaced. After discovering the first jump, a new copper conductor was ordered and installed in July 2020 to correct this anomaly, though without any observable effect. In order to take these jumps into account, we include a maintenance period feature in all welds, indicating to which timespan they belong, as shown in Figure 1. As no rise in the number of rewelds or broken welds was noticed, the question remains whether this effect is due to changes in how the current is measured or whether the welding current effectively dropped without affecting weld quality.  
While imperfect data collection and unexplained data anomalies are undesired, they are not uncommon. Instead, techniques that can deal with these challenges need to be found. As we will demonstrate, our technique can quickly adapt to these changes, whereas state-of-the-art techniques cannot.