Abstract
This article gives an overview of some existing diagnostic automation methods applicable to a variety of subject areas. At present, many branches of industry, medicine, agriculture and agrotechnical economies are moving towards reducing the need for involving human resources in the processes of diagnosing equipment malfunctions, various diseases of both people and plants. The number of different methods for diagnostics and processing the received data increases over time, as do the data flow itself and the requirements for the processing accuracy and speed. An important task is to build adequate models for data analysis, taking into account random perturbations and the need for rapid research at the rate of incoming data. The only way to choose the most optimal method is to conduct a comparative analysis and correlate many factors to be considered when choosing a particular method
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