Increase of repair efficiency is achieved due to the formation of centralized specialized production facilities which implement the vehicle component parts repair technique with the use of industrial technological processes to restore the technical state of the units and their components. In this case, the establishment of the expediency of sending the unit to repair, as well as the defining of volumes and nomenclature for necessary repair actions, should be performed at the stage of pre-repair diagnosis for each individual unit taking into account its actual technical condition. However, the effectiveness of pre-repair diagnosis using both deterministic and probabilistic methods of processing and analyzing the information obtained is significantly reduced by the presence of errors in the recognition of defects and the distribution of aggregates in accordance with the repair work variety preformed at the repair enterprise. Using promising cognitive technology based on neural networks it is possible to completely avoid the losses associated with the repetition of repair work. Therefore, the formation of scientific and methodological bases for the development, training and practical application of artificial neural networks in the subsystems of the pre-repair diagnosis of the repair fund of automobile vehicle omponent parts is an important and urgent task. The paper presents the results of analytical studies and a number of original techniques for the formation of scientific and methodological foundations for the development, training and practical application of artificial neural networks in the process of diagnosis the car vehicle component parts and special oil and gas equipment entering the centralized repair according to their technical condition
Particle Size Distribution Reconstruction Using a Finite Number of Its Moments through Artificial Neural Networks: A Practical Application
