This paper describes the results of work in the field of failure self-diagnostics for automated systems in real time to increase the efficiency of their operation. We describe the method developed of a diagnosis search formation space by applying to the Expert System Knowledge Base to diagnose failures in automated systems. The input data for the Expert Diagnostic System is a conflicting set of diagnostic codes generated by the automated system over the time interval ∆t during its operation. We proposed mathematical methods to work with a data structure “m-tuples based on ordinary sets of arbitrary cardinality n” to process the input data. We conducted a comparative analysis to estimate the execution time of algorithms for the diagnosis search formation space using sequential access to the Boolean of input data and using the method developed. The analysis showed that the application of the proposed method changes the functional dependency of the execution time estimation of the algorithm in accordance with the number of its input data n from exponential to cubic. The application of the method developed allows us to minimize the time needed to establish the diagnosis to real time. The method presented to diagnose automated systems allows creating methods and algorithms for automatic self-recovery of their operability after reversible failures in real time