The system of the preventive maintenance (PM), accepted at production association LLC KINEF, taking into account safe operation and health assessment, is analyzed. Statistical data of results of diagnostics are processed; options of the increase of the integrated reliability indicator of the system of pipeline transport are offered. The trend lines of a condition change of the pipeline in time and the approximate curves of diagnostic data of nondestructive inspection technique with creation of the long-term forecast of its technical condition was constructed. The option of correction of the accepted PM system with the analysis of quantitative change of an indicator of reliability was developed, by which application of the percentage of uptime will increase from 0.909 to 0.957.

This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN‐classifier). Experiments are carried out in la-boratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN‐classifier. A method of providing the data into the classi-fier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data.

This paper presents a numerical simulation of powder sintering. The numerical model presented in this paper uses the discrete element method, which suggests that the material can be modeled by a large set of discrete elements (particles) of a spherical shape that interact with each other. A methodology has been developed to determine the DEM parameters of bulk materials based on machine vision and a neural network algorithm. The approach is suitable for obtaining the exact values of the DEM parameters of the investigated bulk material by comparing the visual images of the material’s behavior at the experimental stand in reality and in the model. Simulation of sintering requires an introduction of cohesive interaction between particles representing interparticle sintering forces. Numerical sintering studies were supplemented with experimental studies that provided data for calibration and model validation. The experimental results have shown a significant capability of the designed numerical model in modeling sintering processes. Evolution of microstructure and density during sintering have been studied under the laboratory conditions.