# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 3 Application Example
### Data Sources  
Despite the growth of this area, due to business competitiveness, sharing sensitive information of this nature is rare, which means that the number of publicly available datasets (relevant to this application) is very scarce. Within an industrial environment, there is a very complete data set, made available by Microsoft, published in (2020), of relevance to the present project. This dataset contains data from five different sources:  
* real-time telemetry;
* error log;
* maintenance history;
* fault history; and
* information about machines  
The data were acquired over a year (2015) for one hundred machines, except for the maintenance history, which also contains records for the year 2014. For a total of one hundred machines, from four different models, the data set contains 876,100 hourly telemetry records, that is, 8761 records per machine. The failure records contain 3919 entries and the maintenance history 3286. The failure history has 761 records, that is, on average, about 8 failure records per machine, throughout 2015. Each machine has 4 components of interest for analysis and also 4 sensors, which measure tension, pressure, vibration and rotation. A controller monitors the system and is able to alert you to the occurrence of 5 types of errors. Thus, real-time telemetry data consists of measurements from different sensors (4 per machine), with the associated date and time. The measurements of voltage (\"volt\"), rotation (\"rotate\"), pressure (\"pressure\") and vibration (\"vibration\") are acquired in real time and the average of these measurements over an hour is recorded in Table 1.  
To better understand the behavior of each sensor, a simple statistical analysis is performed in Table 2, where the mean, the standard deviation, and the minimum and maximum values are calculated for the parameters voltage (\"volt\"), rotation (\"rotate\"), pressure (\"pressure\") and vibration (\"vibration\") during 2015. As an example, Figure 2 shows the graphical evolutions of the Tension signals (Figure 2a), Rotation (Figure 2b), Pressure (Figure 2c) and Vibration (Figure 2d), over the first fifteen days of January 2015, for machine 1 (machineID = 1). The second source of information is the error log. These are errors that did not immediately lead to a failure, as the machine remained operational. There are 5 types of errors: error1, error2, error3, error4, error5. The date and time are rounded to the nearest time. Each record consists of a date/time, machine and type of error--Table 3. The total number of error records over the year 2015 is 3919. In Figure 3 it is possible to observe the number of errors per type over the year 2015. The maintenance records contain data of component replacements resulting from a scheduled or unscheduled maintenance intervention, periodic inspections, or performance degradation. In case of maintenance intervention due to the failure of a component, a fault record is also generated, see next paragraph. For each machine, this data set contains information about 4 types of components: comp1, comp2, comp3, comp4. The date and time are rounded to the nearest time. Each record consists of a date/time, machine and the type of component replaced--Table 4. The total number of maintenance records throughout 2015 is 3286. As previously mentioned, the maintenance records also contain entries for 2014. Figure 4 shows the number of components replaced, by type. It is possible to observe that in this case the number of substitutions is similar for the 4 types of components. The fault records contain the component replacement records, resulting from the maintenance intervention, due to the occurrence of a fault. The data is for the 4 types of components: comp1, comp2, comp3, comp4. The date and time are rounded to the nearest time. Each record consists of a date/time, machine and the type of replaced component--Table 5. The total number of failure records during 2015 is 761.