An article focuses on the development, research and implementation of algorithmic, hard- and software devices for controlling and diagnosing complex dynamic industrial objects. In the course of the research, the industrial object was selected, its main technical characteristics were determined, its base units were examined, and the most vulnerable sites were identified. There were the heaters of the heating zones, which correspond to the infra-frequency processes, and the bearings of the rolling gear reducer, which correspond to the high-frequency processes.
A two-level system with primary and secondary statistical transformations was selected as the control and diagnostic system. The target function of the primary statistical transformation is the auto-coherence function, to which the inputs of time signals from the nodes of the industrial object are received, and at the output, the vector of the component (noise and functional) indicators of the auto-coherence function is obtained by dispersion decomposition. Secondary statistical transformation is implemented on the basis of linear discriminant function, which allows making decisions about the results of control and diagnostic.
The practical implementation of the monitoring and diagnostic system is based on the Arduino NANO microprocessor kit, involving a personal computer and measuring channels of vibration and temperature. LabView software packages were used to perform the control and diagnostic algorithms.
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