## 4 Results and Discussion
If this anomaly is not resolved and continues to evolve, it triggers a more severe notification, an alarm. As soon as the first notification occurs, the maintenance group employees must begin to identify which problem has arisen and whether it can be solved without replacing a component of the machine, then acting on the conclusion that they reached. These limits, together with the values of the collected data, are represented using time-based graphs, which help to detect anomalies in the machines in an intuitive way and without the need for contact with the physical world. * Case 1: Problem-solving in Module 1  
Bearing in mind that Renault machinery is subject to high wear and tear, machines will have to undergo several maintenance operations. An example of this case study is related to the occurrence of an anomaly in the electrical panel during the month of November of 2019. The maintenance group first received an alert notification on all six DMG machines of M1, where they could see that they had higher temperature values than those accepted, which should be below 40 \({}^{\circ}\)C. After an investigation in the shop floor, where the condition of one of the DMG machines was first analyzed, it was found that its ventilation was inadequate, resulting in higher temperature values (Figure 6). This problem was confirmed on the remaining machines, which led to the replacement of the external fans on all six DMG machines. The previous graphs (Figure 6a,b) show the condition of machines before and after the maintenance operation. In Figure 6a, it can be observed that some of the lines, which represent the temperature, are above 40 \({}^{\circ}\)C. From this moment, the engineering team realized that something was wrong with the six DMG machines of M1. In Figure 6b, it is evident that the measures implemented allowed to make corrections on time (i.e., fan replacement) and before it caused significant damage to the Renault industrial machines (i.e., before reaching breaking temperatures). Case 2: Problem-solving in the Differential Box Production Line  
The second case that we present refers to an anomaly detected in the machining line of the differential box (Figure 7), which is also one of the components of the JT4 gearbox (Figure 7a). The production line where the JT4 gearbox is located comprises a total of six machines, of which four are from HESSAP, one MARPOSS, and the other LIEBHERSS brands. Renault Cacia decided to install vibration and temperature sensors on the 4 th HESSAP asset, which had a long history of malfunctions. After installing the sensors, an anomaly was detected in one of the HESSAP machines (Figure 7b), caused by unwanted collisions with the boxes produced. Initially, the maintenance group received an alert notification, and it was possible to observe that the equipment was operating at values above the established alert limit of 7.5 mm/s. After the alarm was activated, the maintenance team went to the site to analyze the vibration values. Based on similar cases previously solved at Renault Cacia, the team estimated that the probability of failure due to the machines' breakage was unlikely, despite having reached vibration values above the alarm limit of 17.5 mm/s. According to the team's estimation, the machine could operate for about six months until the end of its life cycle without compromising the quality of the equipment's operation. The advantage of early identification of non-conformities allowed Renault to have the opportunity to purchase the replacement material on time and select the best day to carry out the intervention on the equipment. So, the mechanism was replaced during a period of reduced activity. The result is twofold, while there was no loss of productivity and the use of the mechanism was maximized, Renault reduced its operational costs. This maintenance action allowed a return of approximately 200% of the initial investment.