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Publications (10 of 11) Show all publications
Abdeljaber, O., Sassi, S., Avci, O., Kiranyaz, S., Ibrahim, A. & Gabbouj, M. (2019). Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring. IEEE transactions on industrial electronics (1982. Print), 66(10), 8136-8147
Open this publication in new window or tab >>Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring
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2019 (English)In: IEEE transactions on industrial electronics (1982. Print), ISSN 0278-0046, E-ISSN 1557-9948, Vol. 66, no 10, p. 8136-8147Article in journal (Refereed) Published
Abstract [en]

This paper presents a fast, accurate, and simple systematic approach for online condition monitoring and severity identification of ball bearings. This approach utilizes compact one-dimensional (1-D) convolutional neural networks (CNNs) to identify, quantify, and localize bearing damage. The proposed approach is verified experimentally under several single and multiple damage scenarios. The experimental results demonstrated that the proposed approach can achieve a high level of accuracy for damage detection, localization, and quantification. Besides its real-time processing ability and superior robustness against the high-level noise presence, the compact and minimally trained 1-D CNNs in the core of the proposed approach can handle new damage scenarios with utmost accuracy.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Reliability and Maintenance
Research subject
Technology (byts ev till Engineering), Industrial economy
Identifiers
urn:nbn:se:lnu:diva-88123 (URN)10.1109/TIE.2018.2886789 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-08-28Bibliographically approved
Abdeljaber, O., Avci, O., Kiranyaz, S., Boashash, B., Sodano, H. & Inman, D. (2018). 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing, 275, 1308-1317
Open this publication in new window or tab >>1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data
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2018 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 275, p. 1308-1317Article in journal (Refereed) Published
Abstract [en]

Structural damage detection has been an interdisciplinary area of interest for various engineering fields. While the available damage detection methods have been in the process of adapting machine learning concepts, most machine learning based methods extract “hand-crafted” features which are fixed and manually selected in advance. Their performance varies significantly among various patterns of data depending on the particular structure under analysis. Convolutional neural networks (CNNs), on the other hand, can fuse and simultaneously optimize two major sets of an assessment task (feature extraction and classification) into a single learning block during the training phase. This ability not only provides an improved classification performance but also yields a superior computational efficiency. 1D CNNs have recently achieved state-of-the-art performance in vibration-based structural damage detection; however, it has been reported that the training of the CNNs requires significant amount of measurements especially in large structures. In order to overcome this limitation, this paper presents an enhanced CNN-based approach that requires only two measurement sets regardless of the size of the structure. This approach is verified using the experimental data of the Phase II benchmark problem of structural health monitoring which had been introduced by IASC-ASCE Structural Health Monitoring Task Group. As a result, it is shown that the enhanced CNN-based approach successfully estimated the actual amount of damage for the nine damage scenarios of the benchmark study.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Other Civil Engineering Computer Sciences
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88125 (URN)10.1016/j.neucom.2017.09.069 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-12Bibliographically approved
Do, N. T., Gül, M., Abdeljaber, O. & Avci, O. (2018). Novel framework for vibration serviceability assessment of stadium grandstands considering durations of vibrations. Journal of Structural Engineering, 144(2)
Open this publication in new window or tab >>Novel framework for vibration serviceability assessment of stadium grandstands considering durations of vibrations
2018 (English)In: Journal of Structural Engineering, ISSN 0733-9445, E-ISSN 1943-541X, Vol. 144, no 2Article in journal (Refereed) Published
Abstract [en]

Annoying vibrations in grandstand structures have been receiving more attention due to the increasing slenderness of the architectural components and the complexity of the crowd loading for engineers. The vibration serviceability checks under these conditions become a challenge in the design and operation stages. Regarding human comfort, excessive vibrations due to occupant activities may affect comfort and/or cause panic, especially for passive occupants who do not participate in generating excitations. Although durations of excessive vibrations have been considered as one of the most important factors affecting occupant comfort, incorporating the vibration duration in the occupant comfort analysis has not been addressed yet. In addition, the currently available approaches using raw acceleration, weighted RMS acceleration, vibration dose values (VDV), and so on may not always be sufficient for serviceability assessment due to the lack of guided procedure for calculating the integration time and implementing the duration of vibration into the process. Therefore this study proposes a new parameter and framework for assessing human comfort which incorporates the duration of vibration with conventional data processing. The aim is to better examine vibration levels and the corresponding occupant response focusing on grandstand structures. A new parameter, the area of RMS (ARMS), is introduced using the running RMS values of acceleration weighted by the frequency weighting functions. Furthermore, perception ranges for human comfort levels based on the proposed parameter are presented. The experimental study reveals that the proposed framework can successfully address the impact of duration time on determining the levels of vibrations and comfort using the proposed parameter.

Place, publisher, year, edition, pages
American Society of Civil Engineers (ASCE), 2018
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88126 (URN)10.1061/(ASCE)ST.1943-541X.0001941 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-18Bibliographically approved
Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M. & Inman, D. (2018). Wireless and real-time structural damage detection: a novel decentralized method for wireless sensor networks. Journal of Sound and Vibration, 424, 158-172
Open this publication in new window or tab >>Wireless and real-time structural damage detection: a novel decentralized method for wireless sensor networks
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2018 (English)In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 424, p. 158-172Article in journal (Refereed) Published
Abstract [en]

Being an alternative to conventional wired sensors, wireless sensor networks (WSNs) are extensively used in Structural Health Monitoring (SHM) applications. Most of the Structural Damage Detection (SDD) approaches available in the SHM literature are centralized as they require transferring data from all sensors within the network to a single processing unit to evaluate the structural condition. These methods are found predominantly feasible for wired SHM systems; however, transmission and synchronization of huge data sets in WSNs has been found to be arduous. As such, the application of centralized methods with WSNs has been a challenge for engineers. In this paper, the authors are presenting a novel application of 1D Convolutional Neural Networks (1D CNNs) on WSNs for SDD purposes. The SDD is successfully performed completely wireless and real-time under ambient conditions. As a result of this, a decentralized damage detection method suitable for wireless SHM systems is proposed. The proposed method is based on 1D CNNs and it involves training an individual 1D CNN for each wireless sensor in the network in a format where each CNN is assigned to process the locally-available data only, eliminating the need for data transmission and synchronization. The proposed damage detection method operates directly on the raw ambient vibration condition signals without any filtering or preprocessing. Moreover, the proposed approach requires minimal computational time and power since 1D CNNs merge both feature extraction and classification tasks into a single learning block. This ability is prevailingly cost-effective and evidently practical in WSNs considering the hardware systems have been occasionally reported to suffer from limited power supply in these networks. To display the capability and verify the success of the proposed method, large-scale experiments conducted on a laboratory structure equipped with a state-of-the-art WSN are reported.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Other Civil Engineering
Research subject
Computer and Information Sciences Computer Science, Computer Science; Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88124 (URN)10.1016/j.jsv.2018.03.008 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-06Bibliographically approved
Abdeljaber, O., Avci, O., Kiranyaz, S. & Inman, D. (2017). Optimization of linear zigzag insert metastructures for low-frequency vibration attenuation using genetic algorithms. Mechanical systems and signal processing, 84(Part A), 625-641
Open this publication in new window or tab >>Optimization of linear zigzag insert metastructures for low-frequency vibration attenuation using genetic algorithms
2017 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 84, no Part A, p. 625-641Article in journal (Refereed) Published
Abstract [en]

Vibration suppression remains a crucial issue in the design of structures and machines. Recent studies have shown that with the use of metamaterial inspired structures (or metastructures), considerable vibration attenuation can be achieved. Optimization of the internal geometry of metastructures maximizes the suppression performance. Zigzag inserts have been reported to be efficient for vibration attenuation. It has also been reported that the geometric parameters of the inserts affect the vibration suppression performance in a complex manner. In an attempt to find out the most efficient parameters, an optimization study has been conducted on the linear zigzag inserts and is presented here. The research reported in this paper aims at developing an automated method for determining the geometry of zigzag inserts through optimization. This genetic algorithm based optimization process searches for optimal zigzag designs which are properly tuned to suppress vibrations when inserted in a specific host structure (cantilever beam). The inserts adopted in this study consist of a cantilever zigzag structure with a mass attached to its unsupported tip. Numerical simulations are carried out to demonstrate the efficiency of the proposed zigzag optimization approach.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88129 (URN)10.1016/j.ymssp.2016.07.011 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-18Bibliographically approved
Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M. & Inman, D. (2017). Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 154-170
Open this publication in new window or tab >>Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
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2017 (English)In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 388, p. 154-170Article in journal (Refereed) Published
Abstract [en]

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88128 (URN)10.1016/j.jsv.2016.10.043 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-18Bibliographically approved
Catbas, C., Celik, O., Avci, O., Abdeljaber, O., Gul, M. & Do, N. (2017). Sensing and monitoring for stadium structures: a review of recent advances and a forward look. Frontiers in built environment, 38(3)
Open this publication in new window or tab >>Sensing and monitoring for stadium structures: a review of recent advances and a forward look
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2017 (English)In: Frontiers in built environment, E-ISSN 2297-3362, Vol. 38, no 3Article in journal (Refereed) Published
Abstract [en]

Stadiums like those used for sporting or concert events are distinct from other civil engineering structures due to several different characteristics. Some challenges mainly originate from the interaction with the human factor, as stadiums are subjected to both synchronized and random motion of large crowds. The investigations in the literature on this topic clearly state that stadiums designs are in urgent need of more reliable load quantification and modeling strategies, deeper understanding of structural response, generation of simple but efficient human–structure interaction models, and more accurate criteria for vibration acceptability. Although many esthetically pleasing and technologically advanced stadiums have been designed and constructed using structurally innovative methods, recent research on this field still calls for less conservative and more realistic designs. This article aims to highlight the recent advances in this field and to provide a follow-up to the literature review covering until 2008 (Jones et al., 2011a) on vibration serviceability of stadiums structures. The article will also discuss new sensing and monitoring techniques on load-time history measurements and their regeneration, as well as crowd motion, stadium health monitoring, and human comfort analysis. Operational effects of crowds on the dynamic properties are also discussed. The article concludes with a forward look on the recommended work and research for dynamic assessment of stadiums.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2017
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88127 (URN)10.3389/fbuil.2017.00038 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-12Bibliographically approved
Abdeljaber, O., Avci, O. & Inman, D. (2016). Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks. Journal of Sound and Vibration, 363, 33-53
Open this publication in new window or tab >>Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks
2016 (English)In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 363, p. 33-53Article in journal (Refereed) Published
Abstract [en]

The study presented in this paper introduces a new intelligent methodology to mitigate the vibration response of flexible cantilever plates. The use of the piezoelectric sensor/actuator pairs for active control of plates is discussed. An intelligent neural network based controller is designed to control the optimal voltage applied on the piezoelectric patches. The control technique utilizes a neurocontroller along with a Kalman Filter to compute the appropriate actuator command. The neurocontroller is trained based on an algorithm that incorporates a set of emulator neural networks which are also trained to predict the future response of the cantilever plate. Then, the neurocontroller is evaluated by comparing the uncontrolled and controlled responses under several types of dynamic excitations. It is observed that the neurocontroller reduced the vibration response of the flexible cantilever plate significantly; the results demonstrated the success and robustness of the neurocontroller independent of the type and distribution of the excitation force.

Place, publisher, year, edition, pages
Elsevier, 2016
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88132 (URN)10.1016/j.jsv.2015.10.029 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-12Bibliographically approved
Abdeljaber, O. & Avci, O. (2016). Nonparametric structural damage detection algorithm for ambient vibration response: utilizing artificial neural networks and self-organizing maps. Journal of Architectural Engineering, 22(2)
Open this publication in new window or tab >>Nonparametric structural damage detection algorithm for ambient vibration response: utilizing artificial neural networks and self-organizing maps
2016 (English)In: Journal of Architectural Engineering, ISSN 1076-0431, E-ISSN 1943-5568, Vol. 22, no 2Article in journal (Refereed) Published
Abstract [en]

This study presentes a new nonparametric structural damage detection algorithm that integrates self-organizing maps with a pattern-recognition neural network to quantify and locate structural damage. In this algorithm, self-organizing maps are used to extract a number of damage indices from the ambient vibration response of the monitored structure. The presented study is unique because it demonstrates the development of a nonparametric vibration-based damage detection algorithm that utilizes self-organizing maps to extract meaningful damage indices from ambient vibration signals in the time domain. The ability of the algorithm to identify damage was demonstrated analytically using a finite-element model of a hot-rolled steel grid structure. The algorithm successfully located the structural damage under several damage cases, including damage resulting from local stiffness loss in members and damage resulting from changes in boundary conditions. A sensitivity study was also conducted to evaluate the effects of noise on the computed damage indices. The algorithm was proved to be successful even when the signals are noise-contaminated.

Place, publisher, year, edition, pages
Elsevier, 2016
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88131 (URN)10.1061/(ASCE)AE.1943-5568.0000205 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-12Bibliographically approved
Abdeljaber, O., Avci, O. & Inman, D. (2016). Optimization of chiral lattice based metastructures for broadband vibration suppression using genetic algorithms. Journal of Sound and Vibration, 369, 50-62
Open this publication in new window or tab >>Optimization of chiral lattice based metastructures for broadband vibration suppression using genetic algorithms
2016 (English)In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 369, p. 50-62Article in journal (Refereed) Published
Abstract [en]

One of the major challenges in civil, mechanical, and aerospace engineering is to develop vibration suppression systems with high efficiency and low cost. Recent studies have shown that high damping performance at broadband frequencies can be achieved by incorporating periodic inserts with tunable dynamic properties as internal resonators in structural systems. Structures featuring these kinds of inserts are referred to as metamaterials inspired structures or metastructures. Chiral lattice inserts exhibit unique characteristics such as frequency bandgaps which can be tuned by varying the parameters that define the lattice topology. Recent analytical and experimental investigations have shown that broadband vibration attenuation can be achieved by including chiral lattices as internal resonators in beam-like structures. However, these studies have suggested that the performance of chiral lattice inserts can be maximized by utilizing an efficient optimization technique to obtain the optimal topology of the inserted lattice. In this study, an automated optimization procedure based on a genetic algorithm is applied to obtain the optimal set of parameters that will result in chiral lattice inserts tuned properly to reduce the global vibration levels of a finite-sized beam. Genetic algorithms are considered in this study due to their capability of dealing with complex and insufficiently understood optimization problems. In the optimization process, the basic parameters that govern the geometry of periodic chiral lattices including the number of circular nodes, the thickness of the ligaments, and the characteristic angle are considered. Additionally, a new set of parameters is introduced to enable the optimization process to explore non-periodic chiral designs. Numerical simulations are carried out to demonstrate the efficiency of the optimization process.

Place, publisher, year, edition, pages
Elsevier, 2016
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88130 (URN)10.1016/j.jsv.2015.11.048 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-12Bibliographically approved
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