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The article considers the urgent problem of identifying the state of an object in order to improve the quality of management. To identify the state of the object, a new method is proposed for analyzing the characteristics of the spectral power of a signal recorded during monitoring, which is characterized by the application of convergent decision production rules of a classification type. It is proposed to carry out convergence based on the analysis of the bispectrum of the acoustic signal emitted by the object and the correlation matrix of the characteristics of the amplitude spectral function. The first approach allows to investigate autocorrelation functions of a signal. The second approach allows to study structure of the organization of a signal. The following are considered: the mathematical apparatus and algorithms for determining specific correlation matrices and the methodology of the decision rules system. Results of synthesis of convergent decisive rules for diagnostics of statuses of an organism at standard forms of bronchitis are given. It is noted that the convergence of decision rules allows to increase the accuracy of identification of the state of the observed object by an average of 5-6% compared to private decision rules and by 10% compared with the classification by an expert person. A distinctive feature is the analysis of not only spectral characteristics, but also the functional relationship between them, which allows us to identify new properties and take into account the states that characterize the change in the functioning of the internal control system as a reaction to external influence as a whole. The obtained research results can be used in the construction of smart expert systems that are part of facility management systems whose state is characterized by emitted acoustic signals.

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