## 5 Multi-Criteria Analysis of Steel Rope Diagnostics Results  
Results of experimental research have a different level of significance. Multi-criteria analysis, in this case, is a tool for optimal solution acceptance in diagnostics. The performed processing of experimental data reveals the power of the applied criteria weight and brings efficiency to the diagnostic procedure. Data for multi-criteria analysis are shown in Table 1.  
Evaluation and comparison were performed in the groups of frequencies: \(f\) = 60, 110, 160, 210, 260, 310, 360, 410, 460, 510, and 560--in total 11 groups. Each group contains nine alternative cases to be compared. Each case has a different rope length and diameter. For comparison, we use four parameters: vibration amplitudes of the rope surface, and three different lengths of the wire, as presented in Table 1. The criteria weight here is defined using three methods: entropy, CILOS, vand IDOCRIW. The obtained criteria weights were compared by applying four MCDM methods: TOPSIS, COPRAS, EDAS and SAW.  
The values of criteria weights are presented in Table 2 (Appendix B). Part of the results are presented graphically in Figures 5 and 6.  
Figures 5 and 6 present comparisons of results that are obtained using two methods: entropy and CILOS. Weights of criteria and their difference range using IDOCRIW. Such procedure occurs to  
Figure 5: Results of the criteria weight distribution for entropy, Criterion Impact Loss (CILOS) and Integrated Determination of Objective Criteria Weights (IDOCRIW) methods for a 60 Hz vibration frequency.  
Figure 6: Results of the criteria weight distribution for entropy, CILOS and IDOCRIW methods for a 110 Hz vibration frequency.  
broaden the distribution of results obtained from entropy and CILOS, as shown in Figure 6 for the case of vibration frequency 110 Hz. In this case, generalized weights of IDOCRIW compensate deficiencies of both methods and closer represent structure of the analyzed data. In the case presented in Figure 6, using entropy, Criterion 4 influences evaluation 25 times more than Criterion 1. Implementation of IDOCRIW decreases this difference to 4.6 times. In the practice presented in Figure 5, criteria weight values differ insignificantly. Even in this case, weights obtained using IDOCRIW better represent structure of the data.  
A comparison of the efficiency of MCDM is presented in Figures 7 and 8. Every MCDM method has its own peculiarities, therefore, it is most efficient use the mean of all methods. It must be noted that all methods give unified characteristics for the first and last places (in our case, the third and eighth tests).  
Figures 7 and \(8\) compare cases of using various MCDM methods. Figure 7 (for the case of 60 Hz) shows a particular case when peculiarities of various MCDM methods cause broad distribution of evaluations and the range used the mean of all method results. Figure 8 (for the case of 110 Hz) presents a very special case of MCDM analysis when all proposed methods are equal in criteria weight. This is very rare in practical cases.  
All the results of the MCDM method evaluation are presented in Table A3 (Appendix C).  
After processing of the experimental data, significant improvement in the damage criteria can be added. This will increase the diagnostic accuracy and reduce the requirements for measurement equipment. Multi-criteria analysis revealed important issues. According to the analysis shown in Table A3, the most successful are Variants 8 and 9. The analysis of the experimental test results requires additional research on the complexity of physical phenomena-based methods. The sensitivity of the applied MCDM methods differs and EDAS seems to be the most sensitive one. The application of multi-criteria analysis in the field of technical diagnostics seems very promising.