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The article discusses the problem of diagnosing technological compressor equipment. The typical causes of defects in screw compressors and the causes of their occurrence are considered. The choice of vibration diagnostics sensors to create a system of complex diagnostics of technological equipment is substantiated. The results of expert evaluation of vibration sensors are presented, which determine the most effective solution for the diagnostic system.

The article discusses the task of identifying a neural network controller for the installation of rectification of oil refining production. A rectification process research model is used to evaluate the effectiveness of the controller. The control parameters of the rectification process that are used to identify the controller and evaluate its effectiveness are determined. In a numerical study, the possibility of using a neural network controller to control the rectification process is shown. As a basic option for a comparative study, we used a PID-regulator, which is the standard version in production today. The advantage of a neural network controller in controlling processes in the context of the implementation of various target trajectories is shown. The proposed model of a neural network controller can be adapted and used for computer control of the rectification process.

Currently, one of the most widely used and effective types of technological equipment is screw compressor equipment. Along with the fact, that such equipment has a number of advantages that determine its high efficiency, it is characterized by increased wear of important structural elements. This can lead to reduced compressor efficiencies and malfunctions that can result in emergencies. In this regard, the paper presents the results of developing a scheme for continuous monitoring of the technical condition of screw compressor units. Variants of installing vibration sensors that provide data collection of vibration diagnostics are determined. In order to automate the analysis of the collected data, it is proposed to use the method of data mining based on neural networks to recognize the technical condition. The results of testing the neural network data method of a real compressor unit are presented.

Журнал зарегистрирован в Управлении Федеральной службы по надзору в сфере связи, информационных технологий и массовых коммуникаций ПИ № ФС77-41672 от 13 августа 2010г. Журнал размещен в открытом бесплатном доступе на сайте www.ntvp.ru, и в Научной электронной библиотеке (участвует в программе по формированию РИНЦ). Журнал включен ВАК РФ в перечень научных журналов, в которых должны быть опубликованы основные научные результаты диссертаций на соискание ученых степеней доктора и кандидата наук. Подписной индекс в объединенном каталоге «Пресса России» № 12025.