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.

Radial charts were commonly used in the industry to allow retrospective assessment of technological parameters. Today it is relevant to digitize the obtained data in order to simplify the automation of technological processes. Digitization of radial charts by means of standard methods is carried out with the help of human labor at significant time costs. The article proposes an automated method for digitizing radial charts using software, developed in the LabVIEW programming environment. The results of processing radial charts are displayed on the screen in numerical and graphical form, and can also be exported to a file (for example, to Notepad or MS Excel). The presented technique can be applied to images obtained on a color or black-and-white scanner, which minimizes geometric distortions, associated with the conversion of a paper document into electronic form, and ensures recognition quality of the clear plot line with an average relative error of up to 3 %. In case of ink fading or perspective photos of the diagram, the value of relative error can reach 8 %, as a result of which additional manual correction of the data will be required.