## 6 Conclusions  
In this work, it was possible to develop a practical and straightforward methodology to evaluate the performance of condition indicators (CIs) in time and frequency domains. Its application was tested for crack detection in railway axes, by using vibration signals measured with accelerometers located on the right and left side of the railway axes, along the vertical, axial, and longitudinal directions. This test bed is made of real components and has been used in previous works for testing other approaches of vibration analysis.  
Two approaches were proposed. Approach 1 only analyzes the CIs extracted from the vibrations signals measured with the accelerometers placed along the longitudinal direction, on the right and left sides of the axes, as an extension for comparing to previous works analyzing this case. Approach 2 uses a data fusion analysis, by combining the CIs extracted from all the six accelerometers.  
Figure 8: Highest classification accuracy achieved per number of CIs.  
Figure 7: Scatter plot using 3 best CIs of CI_DFRL.  
In approach 1, CIs extracted from the accelerometer on the right side in the different domains show slightly better results of classification accuracy than the condition indicators from the accelerometer on the left side. The right side accelerometer time + frequency domain data set (CI_TFR) had the highest performance, with classification accuracy over 97.14%, which is better than the results obtained in previous works. This fact may be due because the drive motor is placed on the right side, and the driven motor is on the left side which provides movement to the rotating wheels.  
In approach 2, the best performance was achieved with CIs in the data set CI_DFRL which combines all the indicators extracted from the six accelerometers. Classification accuracy was over 98.38%, and this result is better than those one by using approach 1, showing that Data Fusion is a good approach to improve accuracy in fault classification.  
The best results in terms of classification accuracy were achieved by using a combination of indicators extracted from time and frequency domains. The CIs named ZC and SCC in the time domain and PKF, skewness, CP2 in the frequency domain, are the common CIs among the top 10 CIs in both approach 1 and approach 2.  
The main advantage of this methodology is that it supports the identification of the relevant CIs for fault detection in different domains of the vibration signal. This allows identifying which CIs should be monitored on-line, mainly the CIs of time domain as they have low computational cost regarding the signal processing.  
As future work, the contribution of each single attribute to improving the accuracy values regarding the crack detection in railway axles will be analyzed. Moreover, the analysis of CIs coming from other application domains will be developed. Additionally, it is expected to have a repository of discriminating and independent features to develop effective data-driven classification algorithms.  
[PERSON]-[PERSON] proposed the idea, [PERSON] and [PERSON] provided the experimental design and data, [PERSON]-[PERSON] implemented the data processing algorithms and wrote a draft of the paper, [PERSON] implemented classification algorithms and performed results analysis, and [PERSON] and [PERSON] supervised the work and reviewed the final manuscript. All authors contributed in the proposed methodology. All authors have read and agree to the published version of the manuscript.  
This research received no external funding, the APC was funded by Universidad Politecnica Salesiana through the research group GIDTEC.  
Authors would like to thank the support provided by the Spanish Government, through the MAQ-STATUS DPI2015-69325-C2-1-R project, and Universidad Politecnica Salesiana through the research group GIDTEC.  
The authors declare no conflict of interest.