## 3 Materials and Methods  
The data used in this investigation are real and representative of the problem addressed. They were provided by a large petrochemical company located in the south of Spain. They were collected from an industrial pumping system, where two pairs of pump motor assemblies were placed in parallel and measured by sensors located in the most significant parts of the pumping system (see Figure 2). The information obtained through sensors in Industry 4.0 varies depending on the characteristics of the equipment to be monitored and phenomena of interest. In the case of motor pumping systems, there are several parameters of interest, as highlighted by [PERSON] et al. [56], who summarized and reviewed the latest research on machine learning-based pump condition monitoring and fault diagnosis as well as the causes of failures. There are many more causes of pump motor assembly failure, and they can be classified into several types [10]:  
* Hydraulic failures: cavitation, pressure pulsation, and thrusts. * Mechanical failures: bearing/seal failure, vibrations, and lubrication. * Thermal degradation: motor bearings and sealing failure. * Other failures: flow and current. The pump and electric motor assembly are connected through a shaft that transmits power. The figure above shows the features measured and their values at the time of measurement. Processing the case study data revealed that the whole pumping system consists of 105,688 data samples, with 52,851 data samples for pump motor assembly NCP1A and 52,837 samples for assembly NCP1B. The relevant variables identified by operational personnel include characteristics that were measured and classified into seven categories:  
* Vibration (mm/s): This variable was measured at nine different points in the pump motor assembly. Variable names: VPNDE, HPNDE, AXIAL, VPDE, HPDE, VMDE, HMDE, VMNDE, and HMNDE. * Temperature of the motor winding (\({}^{\circ}\)C): This feature was measured in each of the three phases of the motor. Variable names: TEMP.FASE U, TEMP.FASE V, and TEMP.FASE W.
* Flow temperature (\({}^{\circ}\)C): This variable was derived from the fluid that enters the two pumps. Therefore, it has the same value for both pumps. Variable name: TEMP.IMP. * Intensity of the motor (A): This parameter was derived from the electrical current that feeds the motor. Variable name: INTENSITY. * Pressure (kg/cm\({}^{2}\)): This parameter was measured at two different points: when the fluid enters the pump (suction pressure) and when the fluid leaves the pump (impulse pressure). Variable names: Suction PRESS.ASP and Impulse PRESS.IMP. * Flow (m\({}^{3}\)/h): This feature corresponds to the flow value that leaves both pumps after the parallel distribution. Variable name: FLOW. * Temperature of the motor bearing (\({}^{\circ}\)C): This is the target variable and was measured on both electric motor bearings, one coupled to the pump and the other on the free  
Figure 2: System configuration of the selected physical asset. The illustration shows the physical asset on the left, with the pump and its power engine connected by a coupling shaft, while the corresponding P&ID is depicted on the right. (**a**) Physical engine pump. (**b**) Partial PID. side of the motor. Variable names: Pump bearing--TEMP.MOTOR; free bearing--TEMP.FREE. Regarding the methodology used, because of the ambition of this research, i.e., seeking to achieve sustainable predictions of asset behavior over larger periods than those considered for short-term prediction purposes, we decided to first elaborate the framework for orchestrating and accommodating the required operations and tools. Then, the framework was applied to the use case, with particular attention paid to the potential constraints or limitations for its general applicability. The general applicability of the proposed system is influenced by several domain-specific and technical factors, which we have carefully considered in its design and evaluation.