Localisation of unknown impact loads on a steel plate using a pattern recognition method combined with the similarity metric via structural stress responses in the time domain

Qiu, Bingsen; Zhang, Meng; Xie, Yaoguo; Qu, Xianqiang; Xu, Li

doi:10.1016/j.ymssp.2019.04.015

Cited by 28 publications

(16 citation statements)

References 31 publications

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“…According to [ 42 ], impact localisation and reconstruction are the two most important parts of the identification procedure on impact force identification. The localisation of impact force involves four steps, which are summarised as data acquisition, feature extraction, region localisation, and force localisation.…”

Section: Prmcsm Algorithm For Impact Force Identificationmentioning

confidence: 99%

“…To realise the reconstruction and localisation of impact loads, Jayalakshmi [ 41 ] formulated the associated inverse analysis as a constraint optimisation problem and then proposed a hybrid adaptive differential search algorithm, which has proven to be robust and effective. Qiu et al [ 42 ] proposed a method to quickly acquire the positions of random impacts by using the cosine similarity of the structural stress responses, which significantly reduced the computational effort and improved the localisation accuracy. Furthermore, the PRMCSM method was verified by a model experiment and a new similarity metric index was presented with the same properties as the cosine similarity [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, an innovative hybrid algorithm is proposed to localise random unknown impact forces. To localise impact forces rapidly, the feasibility of the PRMCSM method has been demonstrated by numerical simulation [ 42 ] and model experiment [ 43 ]. However, according to the experimental data, the localisation of the impact forces is not completely satisfactory.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Research on a Hybrid Algorithm for Localisation and Reconstruction of the Impact Force Applied to a Rectangular Steel Plate Structure

Qiu

et al. 2022

Sensors

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Impact force is the most common form of load which acts on engineering structures and presents a great hidden risk to the healthy operation of machinery. Therefore, the identification or monitoring of impact forces is a significant issue in structural health monitoring. The conventional optimisation scheme based on inversion techniques requires a significant amount of time to identify random impact forces (impact force localisation and time history reconstruction) and is not suitable for engineering applications. Recently, a pattern recognition method combined with the similarity metric, PRMCSM, has been proposed, which exhibits rapidity in practical engineering applications. This study proposes a novel scheme for identifying unknown random impact forces which hybridises two existing methods and combines the advantages of both. The experimental results indicate that the localisation accuracy of the proposed algorithm (100%) is higher than that of PRMCSM (92%), and the calculation time of the hybrid algorithm (179 s) for 25 validation cases is approximately one nineteenth of the traditional optimisation strategy (3446 s).

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Section: Prmcsm Algorithm For Impact Force Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experimental Research on a Hybrid Algorithm for Localisation and Reconstruction of the Impact Force Applied to a Rectangular Steel Plate Structure

Qiu

et al. 2022

Sensors

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“…Load time-history reconstruction and load localization are the main tasks of load identification. 6,7 Many indirect identification methods have been used to solve these two problems, which can generally be divided into frequencydomain methods and time-domain methods. [8][9][10] Generally, there are two types of frequency-domain methods: one is the frequency response function matrix inversion method and the other is the modal coordinate transformation method.…”

Section: Introductionmentioning

confidence: 99%

A feature learning-based method for impact load reconstruction and localization of the plate-rib assembled structure

Chen

Guo

Duan

et al. 2021

Structural Health Monitoring

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Impact load is the load that machines frequently experienced in engineering applications. Its time-history reconstruction and localization are crucial for structural health monitoring and reliability analysis. However, when identifying random impact loads, conventional inversion methods usually do not perform well because of complex formula derivation, infeasibility of nonlinear structure, and ill-posed problem. Deep learning methods have great ability of feature learning and nonlinear representation as well as comprehensive regularization mechanism. Therefore, a new feature learning-based method is proposed to conduct impact load reconstruction and localization. The proposed method mainly includes two parts. The first part is designed to reconstruct impact load, named convolutional-recurrent encoder–decoder neural network (ED-CRNN). The other part is constructed to localize impact load, called deep convolutional-recurrent neural network (DCRNN). The ED-CRNN utilizes the one-dimensional (1-D) convolutional encoder–decoder to obtain low-dimension feature representations of input signals. Two long short-term memory (LSTM) layers and a bidirectional LSTM (BiLSTM) layer are uniformly distributed in this network to learn the relationship between input features and the output load in time steps. The DCRNN is constructed mainly by two 1-D convolutional neural network (CNN) layers and two BiLSTM layers to learn high-hidden-level spatial as well as temporal features. The fully connected layers are placed at the end to localize an impact load. The effectiveness of the proposed method was demonstrated by two numerical studies and two experiments. The results reveal that the proposed method has the ability to accurately and quickly reconstruct and localize the impact load of complex assembled structure. Furthermore, the performance of the DCRNN is related to the number of sensors and the architecture of the network. Meanwhile, the strategy of alternating layout is proposed to reduce the number of training locations.

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“…Acoustic emission (AE)-based structural health monitoring (SHM) systems have become an important tool to provide in-service detection and localization of low-velocity impacts, thus enhancing safety of composite components. Literature presents various AE localization methods developed over the years (Haider et al, 2018;Morse et al, 2018;Qiao et al, 2019;Qiu et al, 2019;Tian et al, 2019;Yin et al, 2019). However, only few AE source identification systems can be used in anisotropic inhomogeneous composite materials, especially if no prior knowledge of the propagating wave field and mechanical properties of the host component are provided (Tobias, 1976;Ciampa and Meo, 2010a;McLaskey, 2010), which is generally common in practical aerospace applications.…”

Section: Introductionmentioning

confidence: 99%

Impact Localization in Composites Using Time Reversal, Embedded PZT Transducers, and Topological Algorithms

Coles

Castro

Andreades

et al. 2020

Front. Built Environ.

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Time reversal is a powerful imaging processing technique that focuses waves at their original source using a single receiver transducer when diffusive wave field conditions are met. This has been successfully proved on various engineering components and materials using elastic waves with surface bonded transducers. This paper investigates the performance of time reversal for the localization of impact sources on fiber reinforced plastic composite structures with embedded piezoelectric sensors. A topologic approach, here named as minimum average method, is proposed to enhance the accuracy of time reversal in retrieving the impact location. Experimental tests were carried out to validate the robustness and reliability of time reversal against traditional topological approaches by altering impulsive responses contained in the baseline signals. Impact localization results revealed that time reversal and the new topological approach provided high accuracy in identifying the impact location, particularly in the presence of double impacts and material damage, which were not accounted during the initial training process. Results indicate that time reversal with embedded transducers has potential to be effective in real operating conditions, where alterations of acoustic emission responses in the baseline signals are less predictable.

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Localisation of unknown impact loads on a steel plate using a pattern recognition method combined with the similarity metric via structural stress responses in the time domain

Cited by 28 publications

References 31 publications

Experimental Research on a Hybrid Algorithm for Localisation and Reconstruction of the Impact Force Applied to a Rectangular Steel Plate Structure

Experimental Research on a Hybrid Algorithm for Localisation and Reconstruction of the Impact Force Applied to a Rectangular Steel Plate Structure

A feature learning-based method for impact load reconstruction and localization of the plate-rib assembled structure

Impact Localization in Composites Using Time Reversal, Embedded PZT Transducers, and Topological Algorithms

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