The effect of the size and position of transverse cracks in the natural frequencies of the rods

Vankov, Yuri; Ziganshin, Sh G.; Izmailova, E V; Zagretdinov, A. R.

doi:10.1088/1757-899x/86/1/012036

Cited by 11 publications

(4 citation statements)

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“…In order to classify the bearings into defect-free or defective class (Figure 4), the approach is proposed that is used to control products by the method of free oscillations [11][12][13][14] characteristic of the procedures for anomaly rejection: the analysis algorithm interprets a set (Table 1) for defect-free (bearing number 1-2) and defective rolling bearings (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) with the amplitude of the noise component below a) and above b) defect frequency amplitude. The comparison of signal scale graphs by PSNR metrics was performed based on forty types of wavelet basis, such as Meyer, Morlet, MexicanHat, Haar, db02, db03, db04, db05, db06, db07, db08, db09, db10, db11, db12, db13, db14, sym2, sym3, sym4, sym5, sym6, sym7, sym8, coif1, coif2, coif3, coif4, coif5, bior1_3, bior1_5, bior2_2, bior2_4, bior2_6, bior2_8, bior3_1, bior3_3, bior3_5, bior3_7, bior3_9 (Table 2) [12,13].…”

Section: Resultsmentioning

confidence: 99%

“…Figure 6 provides five values of the comparison results concerning the wavelet scaling graphs of the simulated signals are presented (Table 1) with a standard scaling graph, the PSNR metrics, Mexican hat as wavelet basis, defect-free (bearing number 1-2) and defective rolling bearings (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) with the amplitude of the noise component below a) and above b) the amplitude of defect frequencies. …”

Section: Resultsmentioning

confidence: 99%

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Rolling bearing monitoring algorithm by wavelet scaling comparison

Rinatovich¹,

Vitalevich²,

Victorovich³

et al. 2018

J. Fundam and Appl Sci.

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A rolling bearing is the most common element of any rotary mechanism design and, at the same time, the most vulnerable element that determines the operability and the durability of equipment. The purpose of the work is to develop the method of bearing condition control by vibration parameters on the basis of scale graph comparison of a continuous wavelet analysis.The goal is achieved by the solution of the following tasks: to determine informative criteria that allow to detect the bearing defects by the analysis of its vibration; to develop the algorithm of bearing defect detection by the parameters of their vibrations, which make it possible to control products in automatic mode.They performed the simulation of whole and defective rolling bearing signals by the synthesis of frequency components characteristic for defects.The correlation coefficient of spectra and the rank correlation of Spearman's spectra were used in the work for spectrum comparison. The comparison of the scaling graphs obtained by continuous wavelet transformation was carried out using PSNR metric.The approach is suggested in order to separate the bearings into "defect-free" or "defective" S. E. Rinatovich et al. J Fundam Appl Sci. 2017, 9(2S), 957-971 958 estimate p is calculated; the variance estimate S is calculated as the median of absolute deviations with respect to a position estimate; a confidence interval is developed for a given level of significance  . The bearings with comparison results that fall within the confidence interval are considered defect-free ones, and the ones that didn't fall within this interval are considered as defective.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Rolling bearing monitoring algorithm by wavelet scaling comparison

Rinatovich¹,

Vitalevich²,

Victorovich³

et al. 2018

J. Fundam and Appl Sci.

View full text Add to dashboard Cite

show abstract

“…Recording and processing of acoustic signals is carried out using a specially developed package of applied programs Inspector (A.S. No. 2009612056) [6,7,9]. The measurements were carried out for both a defect-free pipeline and a pipeline with artificially applied defects in the form of holes with diameters of 7 mm, 11 mm and 15 mm (Fig.…”

Section: Methodsmentioning

confidence: 99%

Assessment of Technical Condition of Polyurethane Foam Thermal Insulation Pipelines of Heating Networks Using Neural Network Technologies

Сайфуллин¹,

Ziganshin²,

Vankov³

et al. 2018

IJET

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Pipelines of heat networks are an important element of heat supply to cities and industrial facilities. To increase the reliability of the operation of pipelines of heating networks, reducing the number of their accidents and increasing the economic parameters of transportation of heat energy, it is required to constantly increase the volumes and quality of complex diagnostics. The instruments currently used for the diagnosis of pipelines have many shortcomings. Among them, low reliability of detection of defects and subjectivity of decision-making, as well as lack of funds for diagnostics of pre-insulated pipelines (in polyurethane foam insulation). To simplify, accelerate and improve the reliability of monitoring the technical condition of pipelines, the authors set the goal of diagnosing the object of research using acoustic methods, using neural network technologies to process acoustic signals. The article describes experimental studies of pipelines of heating networks in polyurethane foam insulation with various sizes of defects and an analysis of the acoustic signals obtained at the same time is made. The frequency of natural oscillations of the pipeline is chosen as the determining parameter of the acoustic signal. To process and analyze the frequencies obtained as a result of the experiments, a neural network of back propagation of the error was constructed.The results of the classification of the neural network of back propagation of the error trained by the neural network showed its good ability to analyze unknown samples and a high degree of reliability of their recognition.

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“…In this technology, the source of information about the technical state of machine and mechanism components can be a vibration-acoustic signal, the changes of its properties are correlated with changes in the parameters of a technical state caused by unit degradation, the changes in the geometric dimensions of parts, maintenance parameters, etc. [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Rolling bearings control by comparing the wavelet of scaling

Rinatovich

Vitalevich

Victorovich

et al. 2018

J. Fundam and Appl Sci.

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The control of production processes, the quality of machine and mechanism assembly during their manufacture and during the repair period saves labor time and labor costs. Analysis of statistical data on equipment failures in various industries has shown that a large number of failures is conditioned by rolling bearings. The solution of bearing early diagnosis problem will predict reasonably the failure-free operation of the equipment in accordance with the actual condition of the bearing units. The purpose of the study is to develop the method and an information and measuring system to monitor the technical condition of bearings based on vibration parameters based the comparison of scaling graphs of continuous wavelet analysis. The results of vibration acoustic signals of rolling bearings are presented with the use of wavelet transformations, the scaling graphs of signals from rolling bearings with various defects are obtained and analyzed experimentally. The method for bearing monitoring is proposed, which makes it possible to automate the process of defect detection and to increase the resolving power during vibrationacoustic control performance. The result of the study showed that the application of analysis algorithms with the use of wavelet transformation allows to detect the defects of rolling bearings at an early stage of nucleation. Implemented by the proposed control methodology, the measuring system can be used both as in-built into a monitored system and in a mobile version.

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The effect of the size and position of transverse cracks in the natural frequencies of the rods

Cited by 11 publications

References 0 publications

Rolling bearing monitoring algorithm by wavelet scaling comparison

Rolling bearing monitoring algorithm by wavelet scaling comparison

Assessment of Technical Condition of Polyurethane Foam Thermal Insulation Pipelines of Heating Networks Using Neural Network Technologies

Rolling bearings control by comparing the wavelet of scaling

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