Force identification in bolts of flange connections for structural health monitoring and failure prevention

Nazarko, Piotr; Ziemiański, Leonard

doi:10.1016/j.prostr.2017.07.142

Cited by 27 publications

(21 citation statements)

References 6 publications

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“…On the basis of the trained ANN, axial force prediction was carried out for bolts that were not equipped with force sensors. The values obtained were consistent with those of other bolts, which was not possible in previous studies [11,17]. In this work, the dimensionality reduction of measured wave signals (neural network encoder) gave meaningful improvement to the results of the identification of the force in the bolts.…”

Section: Discussionsupporting

confidence: 89%

“…However, the issue of cyclic loading is very important, and further research in this area is needed. Examples of experimental studies carried out so far and the preliminary results of the axial forces' identification can be found in the literature [11,17].…”

Section: Flange Connections Under Static Testsmentioning

confidence: 99%

“…In this way, the elastic wave signals, each containing 10,002 points, after decimation at the rate r = 2, were reduced to only 12 principal components. On the basis of the preliminary test results [11], it turns out, however, that the principal components alone may not be sufficient for proper training of the diagnosis system. The point is for the ANNs to acquire generalization capabilities that enable the prediction of force values in bolts in which no control measurements were carried out.…”

Section: Signal Analysismentioning

confidence: 99%

“…In previous works [11,17], it was assumed that data from all bolts (2, 4, 5) were to be used for the purpose of ANN training, assuming a constant distribution of patterns for testing and validation. The new approach proposed herein involves the learning, testing, and validation patterns being separated into individual bolts from a given connection.…”

Section: Force Prediction In Single Connectionsmentioning

confidence: 99%

“…The results presented in the article concern a series of laboratory tests carried out on flange connections under tension in a static test machine. It appeared that force changes have an impact on the signals measured by the sensors [11]. This was reflected also in the calculated principal components, which are often used to compress signals (see Nazarko [12] or Chen et al [13], where other methods concerning signal compression were compared).…”

Section: Introductionmentioning

confidence: 99%

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Application of Elastic Waves and Neural Networks for the Prediction of Forces in Bolts of Flange Connections Subjected to Static Tension Tests

Nazarko¹,

Ziemiański²

2020

Materials

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There is a group of measurement techniques that can be used in the task of force identification in steel bolts. In this paper, the potential of elastic wave propagation signals was studied for possible application in force monitoring systems. A series of laboratory tests was carried out on flange connections subjected to static tensile tests. Each one contained six screws of the same diameter. Four bolts were equipped with washer load cells. Alternatively, selected bolts were equipped with piezoelectric transducers (actuator and sensor) in order to measure the elastic wave signals. Principal components analysis, time of arrival, and neural network compression were used for dimensionality reduction of the measured signals. Examples of the obtained results with respect to the studied connections show that the tension forces in bolts can be estimated with relatively good accuracy.

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Section: Discussionsupporting

confidence: 89%

Section: Flange Connections Under Static Testsmentioning

confidence: 99%

Section: Signal Analysismentioning

confidence: 99%

Section: Force Prediction In Single Connectionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Elastic Waves and Neural Networks for the Prediction of Forces in Bolts of Flange Connections Subjected to Static Tension Tests

Nazarko¹,

Ziemiański²

2020

Materials

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Distribution of the friction coefficient in the tension connections by the HV set using the combined method

Dubiel¹,

Osetek

Majka

2021

ce papers

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The use of bolts in steel structures nowadays is one of the cheapest, fastest and easiest solutions for joining elements. HV high‐strength bolts used in tension connections are a special type of fasteners covered by supervision beginning with the selection of the material from which they will be made through the production process and quality confirmation, ending with clearly defined assembly requirements. The tests were focused on two assortments of M16 and M24 bolt assemblies made according to EN 14399. A test of the suitability of the connection was performed, expanding it with tests of the coefficient of friction on the bolt thread and between the nut head and washer according to EN ISO 16047. The dynamic joint load tests showed that the distribution of the coefficient of friction on the thread and between the nut and the washer affects the degree of tension through the preloading force. Tensile tests were carried out on the both aforementioned assortments. The determined yield point of the bolt was analyzed, which was referred to the stress causing plastic deformation in the test of connection suitability. Bolts in two systems of forces were compared with each other (simple ‐ stretching and complex ‐ stretching and torsion) and the difference between them was shown. Based on the correlation of results from both tests, the importance of the K‐factor on the mechanical properties of the HV set was emphasized. The dependence of the K‐factor on the coefficients of friction in a bolted joint has been demonstrated. HV high‐strength bolt sets consider assembly procedures using a combined method according EN 1090‐2. Cases of exceeding the yield point of the bolt during assembly and permanent deformation of the fastener have been shown. The scope of assembly was determined in the elastic range and the transition to the range of plastic deformations. Again, reference was made to the coefficient of friction and its effect on the difference in the obtained preloading force leading to risky assembly in the plastic range.

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A Review on the Bolted Flange Looseness Detection Method

Radzi¹,

Zohari²

2022

Lecture Notes in Electrical Engineering

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Force identification in bolts of flange connections for structural health monitoring and failure prevention

Cited by 27 publications

References 6 publications

Application of Elastic Waves and Neural Networks for the Prediction of Forces in Bolts of Flange Connections Subjected to Static Tension Tests

Application of Elastic Waves and Neural Networks for the Prediction of Forces in Bolts of Flange Connections Subjected to Static Tension Tests

Distribution of the friction coefficient in the tension connections by the HV set using the combined method

A Review on the Bolted Flange Looseness Detection Method

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