Using Acoustic Methods for Monitoring the Operating Modes of the Electric Drive in Mobile Objects

Kondratieva, I. U.; Rudakova, Hanna; Polyvoda, Oksana

doi:10.1109/msnmc.2018.8576296

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…An important task is to form a procedure for determining the frequency range of the informative part of the signal based on the estimation of the normalized energy spectrum and spectral entropy. The sequence of stages of determining the boundaries of the informative part of the signal based on spectral entropy [5] is shown in Fig. 1.…”

Section: Spectral Analysis Of Acoustic Signals Noises Of Devices and Machines Charac-mentioning

confidence: 99%

Research of Acoustic Signals Digital Processing Methods Application Efficiency for the Electromechanical System Functional Diagnostics

Rudakova

Polyvoda

Kondratieva

et al. 2021

Lecture Notes in Computational Intelligence and Decision Making

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The article is devoted to researching of application efficiency of acoustic signals digital processing methods for the electromechanical systems (EMS) functional diagnostics in real time. The steps according to which it is necessary to carry out the analysis of the acoustic signals generated during operation of EMS for construction and adjustment of functional diagnostics systems are offered. The primary task is to study the statistical properties of acoustic signals. The next step is spectral analysis of acoustic signals. In the process of acoustic signal conversion after its hardware processing, the limits of the informative part of the signal are determined on the basis of spectral analysis based on Fourier transform of the signal. It is proposed to use logic-time processing based on the estimation of the normalized energy spectrum and spectral entropy to determine the frequency range of the informative part of the signal. The expediency of using the autoregressive model of the moving average as an acoustic signal model is substantiated. The problem of building mathematical models on the basis of which it is possible to adequately identify the intensity of work and the state of the equipment is solved. The procedure for identifying the parameters of the signal model by the recurrent least squares method is given, which allows to analyze the state of the equipment in real time. It is proposed to use multiplescale analysis to speed up the process of signal analysis and reduce the amount of calculations.

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Section: Spectral Analysis Of Acoustic Signals Noises Of Devices and Machines Charac-mentioning

confidence: 99%

Research of Acoustic Signals Digital Processing Methods Application Efficiency for the Electromechanical System Functional Diagnostics

Rudakova

Polyvoda

Kondratieva

et al. 2021

Lecture Notes in Computational Intelligence and Decision Making

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“…. To achieve greater accuracy in identifying the controlled parameter, it is advisable to combine autoregression and moving average in one model [10]. Then the analyzed time series of controlled parameters can be presented in the form of a model:…”

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confidence: 99%

Method of the Ship Main Engine Condition Operational Diagnostics

Polyvoda,

Simanenkov

2023

SBKSMA

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The article is devoted to solving the problem of improving the quality of operational diagnostics of the ship main engine in real time. Modern technical diagnostic systems must perform quick and high-quality identification of increasing malfunctions for the most effective use of monitoring results when solving operational tasks with the issuance of recommendations, which will allow expanding the competence of technical personnel in the decision-making process. Therefore, an important task is the development of mathematical models of time series of measured values of controlled parameters, which will allow to improve the procedure of operational diagnostics due to the detection of the probability of failure of ship engine units before the area of the most intense wear or destruction. In order to improve the existing methods of technical diagnostics of technological equipment, effective operational diagnostics algorithms have been developed, which are implemented in software modules and fully take into account technical and economic requirements, stochastic nature of external influences. When building the operational diagnostics algorithms, the specifics of the processes taking place in the ship main engine were taken into account, modern techniques and methods of mathematical modeling and information theory were used. On the basis of the values obtained as a result of measurements of the controlled parameters of the ship main engine, autoregressive models of the moving average were selected, which describe the obtained time series as accurately as possible. The parameters of the autoregression models were identified using the method of least squares. A method of operational diagnostics based on the determination of spectral entropy and the procedure of logical-time processing is proposed. On the basis of the developed mathematical models and the proposed diagnostic method, an automated system of operational diagnostics of the state of the ship main engine has been developed, which allows timely detection of critical modes of operation of technological equipment in real time.

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Digital Acoustic Signal Processing Methods for Diagnosing Electromechanical Systems

Polyvoda

Rudakova

Kondratieva

et al. 2019

Advances in Intelligent Systems and Computing

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Using Acoustic Methods for Monitoring the Operating Modes of the Electric Drive in Mobile Objects

Cited by 3 publications

References 2 publications

Research of Acoustic Signals Digital Processing Methods Application Efficiency for the Electromechanical System Functional Diagnostics

Research of Acoustic Signals Digital Processing Methods Application Efficiency for the Electromechanical System Functional Diagnostics

Method of the Ship Main Engine Condition Operational Diagnostics

Digital Acoustic Signal Processing Methods for Diagnosing Electromechanical Systems

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