## 3 Evidential Reasoning Algorithm
### Analysis Results and Uncertainty Review  
The observed baseline or general attributes, i.e., assessment evaluations, may be subject to some uncertainty, i.e., incomplete knowledge of the information, which negatively affects the assessment procedure itself. The degree of uncertainty depends on the amount of information, i.e., the available data, the assessor's knowledge of the equipment and devices installed in the observed object and the possibility of a complete assessment of individual attributes. The examples clearly show that some attributes can be fully assessed with an appropriate rating, but that there is always a certain part of the information for which the occurrence of uncertainties cannot be avoided and that these uncertainties can sometimes be significant. The analysis of data collected by the system's technical diagnostics and the linking of appropriate assessments and decisions must be performed in an objective, reliable, repeatable and transparent manner. The above conditions can be satisfied by using an improved evidential reasoning algorithm as a decision-making support tool in a multi-attribute environment [3; 23; 33; 39]. The improved evidential reasoning algorithm satisfies all four axioms of synthesis [34; 37] and allows attributes to participate in the assessment based on their weights. The example clearly demonstrates the different influences of attributes due to the difference in their weights, i.e., different individual weight ratings. In most cases, the choice of weight ratings is the result of the assessor's knowledge of the technical system and the production process. It is important to note that the weighting coefficients and rules used to convert the rating of basic attributes are not generally accepted technical guidelines but are a proposal of the team of the authors to present the method. The weighting ratings and conversion rules should be carefully considered and selected for each observed technical system. The improved evidential reasoning algorithm is resistant to incomplete information about baseline or general attributes, which is an important property that provides an insight into the impact of incomplete knowledge of information related to the baseline attribute and the overall uncertainty in the evaluation of the entire technical system. The uncertainty of the final assessment must be within reasonable limits for the overall system assessment to be meaningful. It is also possible to display the result of the condition assessment as a single numerical value with an interval between the minimum and maximum values rather than an assessment spread over several predefined grades. Such a numerical value allows an easy insight into the state of the system and comparison of the state of the observed system with other similar systems or with the state of the same technical system at another time. Special emphasis is placed on the correct interpretation of physical quantities obtained from measurements of system parameters. It is necessary to adequately process the quantitative information collected and transfer it to the qualitative domain. This information is ranked and used as input to the aggregation process. If an error occurs at this stage, it will affect the final score depending on the individual weighting of the attribute. If the collection of information and the evaluation of the condition is done continuously or at regular time intervals, there is enough information to represent the condition of the observed object as a function of time. Based on the analysis of such a time function, it would be possible to make maintenance-related decisions. In cases where the condition of the technical system is described using a time function and an appropriate knowledge base, it is possible to predict situations that are amenable to failure. This significantly reduces the possibility of malfunctions with serious consequences for the condition of the system.