## 2 Data Collection and Preprocessing
### Preprocessing Methods
On the basis of the conducted research on the impact of the data preparation method on the effectiveness of the diagnostics of the elevator cabin door drive system faults, it can be concluded that the method proposed by the authors, which assumes the use of mean values and standard deviations during the analysis, makes it possible to reduce the number of samples needed for modeling while retaining much of the useful information regarding the operation of the device in certain time intervals, of which a time window length of 7.4 s can be considered a good compromise between linear algorithms and a decision tree. It is worth noting that the operation of the elevator's cabin door is cyclic in nature, related to opening and closing. The above observations led to the development of a new method of data preparation to reduce the amount of transmitted data. Figure 6: Schematic of the third processing method that introduces new features representing the mean and standard deviation for different time window widths and a sampling rate of 2.3 Hz. Another of the processing methods developed involves aggregating a given cabin door cycle into a single record containing information about the mean and standard deviation of all diagnostic parameters. This means that the number of samples in the dataset is reduced to the number of cabin door operating cycles, while retaining useful information regarding the values of diagnostic parameters in the form of statistics. The method involves dividing the dataset into the operating cycles of the drive system and then replacing the diagnostic quantities of each cycle with their averages and standard deviations, as presented in Figure 8. In addition, a new parameter related to the length of the operating cycle was introduced. To ensure that the classification results are independent of the cycle order, the processed dataset was randomly shuffled. The results obtained for the described method are presented in Figure 9.  
Figure 8: Schematic of a processing method that reduces the number of samples by applying data aggregation within the cabin door operation cycle using statistical features. Figure 7: Performance metrics for fault classification by linear regression, logistic regression and decision tree algorithms using a processing method that introduces new features representing the mean and standard deviation for different time window widths and a sampling rate of 2.3 Hz. It can be seen that the proposed processing method produces much better fault classification results while reducing the amount of data from more than 3.9 million records to about 6700 (the number of cycles). Thus, it can be concluded that despite doubling the number of features, it was possible to reduce the amount of data by more than 300 times, and the required data transfer rate has been reduced to just 105 b/s (22 4-byte variables plus CAN overhead, every 11.7 s--the average cycle time), which is 0.08% of the available throughput of the elevator internal bus, which translates into about 34 MB of transfer per month and just over 0.4 GB of required disk space per year for one elevator. The proposed method significantly reduces the amount of diagnostic data transferred, which is beneficial, especially given the need to use the communication infrastructure of the elevator control system to transfer data between components and the device sending data to the central server.