# Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending
## 3 Data Generation  
In the process of analyzing and pre-processing the data from the bending machine, several important steps were followed to understand the signal coming from the programmable logic controller (PLC) that controls the process. Firstly, the machine signals were captured with a low sampling frequency of around 2 hertz, as requested by the operator. Once the bending process was completed, all control data were available in the form of 64 Boolean variables and 34 analog variables, which could be plotted using encoding.  
However, before starting the data analysis, a preliminary exploration of the data was conducted to eliminate signals that did not exhibit variation over time. Once the data were cleaned, a more detailed exploration of the signals was carried out to identify possible correlations between different variables. In this initial exploration, it was observed that out of the 34 analog variables, only 9 showed variations over time, while out of the 64 Boolean variables, only 6 exhibited significant variations, of which only 3 had a physical meaning. The following Table 2 summarizes the main variables under study.  
These variables identified in the preliminary exploration were analyzed using a correlation matrix between variables, aiming to detect collinear or highly correlated variables. Collinear variables can have a negative impact on the results of subsequent analysis, so it is important to identify and potentially eliminate them. This preliminary analysis was crucial to identify the most relevant variables and effectively focus subsequent data analysis studies. In summary, Figure 2 illustrates the necessary steps to perform a preliminary analysis of a set of analog data acquired from a bending machine. The process starts with signal acquisition, continues with signal conditioning and digitization, and concludes with variable selection and correlation matrix analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Definition** & **Variable Type** & **Comment** \\ \hline H\_F & General-fault lamp & bool & * no info \\ H\_MP\_AV & Front main-motor light & bool & \\ H\_MP\_RE & Rear main-motor pilot light & bool & \\ H\_MP\_F & Main-motor failure lamp & bool & * no info \\ H\_E\_On & Operational main-motor lubrication lamp & bool & * no info \\ H\_F\_On & Released-main-motor-brake lamp & bool & \\ Z\_MP\_V & Main-motor speed control & float & \\ Z\_B1\_I & Monitoring of pump 1 current & float & \\ Z\_B2\_I & Monitoring of pump 2 current & float & \\ Z\_CI & Left-wedge monitoring & float & \\ Z\_CD & Right-wedge monitoring & float & \\ Z\_CPI & Left-rear-cylinder monitoring & float & \\ Z\_CD & Right-rear-cylinder monitoring & float & \\ Z\_CPI\_P & Left-rear-cylinder pressure monitoring & float & \\ Z\_CPD\_P & Right-rear-cylinder pressure monitoring & float & \\ \hline \hline \end{tabular}  
* These data do not contain information about the bending process.  
\end{table}
Table 2: Variables sensitive to the bending process.  
The authors have undertaken a detailed examination of signals sensitive to the bending process, strategically excluding those displaying strong correlations to prevent an excess of stored data. This is a crucial aspect in this optimization effort. Following this, an in-depth analysis of the temporal signal of variables of interest has been conducted to identify and eliminate periods when the machine is not in operation, achieved through an examination of internal signals. Subsequently, the continuous and distinct segments of sheet movement during bending have been meticulously split, a process referred to as peak in this paper, enhancing the precision and clarity of data segmentation.