Looseness diagnosis method for connecting bolt of fan foundation based on sensitive mixed-domain features of excitation-response and manifold learning

Chen, Renxiang; Chen, Siyang; Yang, Lixia; Wang, Jiaxu; Xu, Xiangyang; Luo, Tianhong

doi:10.1016/j.neucom.2016.09.041

Cited by 32 publications

(18 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was noticed that force changes influence signals measured by sensors. It was reflected also in calculated principal components, which are often used to compress the signals, see Nazarko [8] or Chen et al [9], where also other signal compression methods are compared. The obtained patterns and data base were then used for the training of Artificial Neural Networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

Axial force prediction based on signals of the elastic wave propagation and artificial neural networks

Nazarko

2019

MATEC Web Conf.

View full text Add to dashboard Cite

The identification of internal forces is not only important to preserve the structure integrity but also to understand how their certain elements and connections work. Two examples of laboratory test are discussed in this paper. The first is related to an aluminium rod mounted in a stand where compression load was applied. Due to the relaxation phenomenon force prediction becomes even more important in case of compressed bolts. Thus, the second example is related to a bolted flange connection during static tensile test. Four out of six bolts were equipped with washer load cells. Alternatively, selected bolts were equipped with piezoelectric transducers (actuator and sensor) in order to measure signals of elastic waves. It was noted that the load increasing causes changes in the measured signals. Principal components analysis was used for dimensionality reduction of measured signals. The aim of this study is to investigate the use of elastic waves and artificial neural networks for the purpose of the force of identification. Examples of preliminary results have shown that axial forces may be estimated with relatively good accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Axial force prediction based on signals of the elastic wave propagation and artificial neural networks

Nazarko

2019

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…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). This article presents new results where ANNs were used both for signal compression and as an inference tool.…”

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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.

show abstract

“…The problem of detecting changes in connections is also dealt with in the aviation industry. For example, in work [21], looseness of connectors in the turbine-plane connection was detected, and in work [22] a decision system for monitoring the condition of bolted joints for large-size aluminum plates used in aircraft plating was proposed. The system was developed on the basis of Lamb wave propagation analysis; the waves were recorded by a piezoelectric transducer system.…”

Section: Introductionmentioning

confidence: 99%

Detection of Anomaly in a Pretensioned Bolted Beam-to-Column Connection Node Using Digital Image Correlation and Neural Networks

2020

View full text Add to dashboard Cite

Bolted connections, commonly applied in civil engineering structures, have many advantages. According to current trends, bolted connections in steel structures are designed as prestressed ones. Unfortunately, precise control of the prestressing forces is difficult, while the loosening (due to, e.g., dynamic interactions) may be dangerous for the entire structure. There are many control methods applied in the determination of the tightening level, among which are vision-based methods. The methods described so far enable—thanks to image processing—damage detection in connections with visible connectors. The level of the considered loosening was significant—in many cases, changes in connectors were visible with the naked eye, whereas the procedure presented here enables the detection of very small changes, impossible to detect without manual inspection of every single connector. It is not necessary to observe the connectors directly, but the near surrounding of the node should be visible. As a measurement technique, Digital Image Correlation (DIC) was used. The applied measurement method and the high sensitivity of the presented procedure makes the presented research original. The currently presented procedure, employing Artificial Neural Networks, based on the laboratory examination of an example of one selected beam-to-column connection of a two-story steel portal frame, was perfect in the detection of a change and in the determination of the number of loosened rows, 95%, and their location, 94%, with the number of false alarms below 1%.

show abstract

Looseness diagnosis method for connecting bolt of fan foundation based on sensitive mixed-domain features of excitation-response and manifold learning

Cited by 32 publications

References 39 publications

Axial force prediction based on signals of the elastic wave propagation and artificial neural networks

Axial force prediction based on signals of the elastic wave propagation and artificial neural networks

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

Detection of Anomaly in a Pretensioned Bolted Beam-to-Column Connection Node Using Digital Image Correlation and Neural Networks

Contact Info

Product

Resources

About