Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

Avcı, Onur; Abdeljaber, Osama; Kıranyaz, Serkan; Hussein, M.F.M.; Inman, Daniel J.

doi:10.1016/j.jsv.2018.03.008

Cited by 178 publications

(92 citation statements)

References 43 publications

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“…The damage was detected with very high accuracy (94.57%), proving that CNNs are suitable for vibration-based SDD. Abdeljaber, Avci, Kiranyaz, Gabbouj, and Inman (2017) and Avci, Abdeljaber, Kiranyaz, Hussein, and Inman (2018) identified the structural damages caused by loosened bolts on a steel frame by a CNN-based approach with very good performance. Another CNN-based SDD experiment was conducted on a benchmark frame structure (Abdeljaber et al, 2017), where structural damages were created by removing some braces.…”

Section: Introductionmentioning

confidence: 99%

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Zhang

Miyamori

Mikami

et al. 2019

Computer aided Civil Eng

219

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Deep learning has ushered in many breakthroughs in vision‐based detection via convolutional neural networks (CNNs), but the vibration‐based structural damage detection by CNN remains being refined. Thus, this study proposes a simple one‐dimensional CNN that detects tiny local structural stiffness and mass changes, and validates the proposed CNN on actual structures. Three independent acceleration databases are established based on a T‐shaped steel beam, a short steel girder bridge (in test field), and a long steel girder bridge (in service). The raw acceleration data are not pre‐processed and are directly used as the training and validation data. The well‐trained CNN almost perfectly identifies the locations of small local changes in the structural mass and stiffness, demonstrating the high sensitivity of the proposed simple CNN to tiny structural state changes in actual structures. The convolutional kernels and outputs of the convolutional and max pooling layers are visualized and discussed as well.

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Section: Introductionmentioning

confidence: 99%

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Zhang

Miyamori

Mikami

et al. 2019

Computer aided Civil Eng

219

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“…It was noticed that the process of generating the data required to train the 1D CNNs in [50,52,71] requires a large number of measurement sessions especially for a large civil structure. Therefore, Avci et al in [53] and then Abdeljaber et al in [54] developed a novel approach based on 1D CNNs, which require significantly less effort and labeled data for training.…”

Section: Figure 11mentioning

confidence: 99%

“…Damaged joint acceleration signal Accelerometer to record acceleration signal Loosened bolts at the joint Figure 12: The test setup and wireless sensors used in [50,71].…”

Section: Loosened Boltsmentioning

confidence: 99%

1-D Convolutional Neural Networks for Signal Processing Applications

Kıranyaz

İnce

Abdeljaber

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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272

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During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.

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“…To address these drawbacks, compact 1D CNNs have been recently developed to operate directly and more efficiently on 1D signals. They have displayed a fast and accurate performance in several real-time monitoring applications such as classification of electrocardiogram (ECG) beats [19], structural health monitoring [18], [31]- [33], and motor fault detection [34]. Additionally, two recent studies have utilized 1D CNNs for damage detection in bearings [35], [36].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring

Abdeljaber

Sassi

Avcı

et al. 2019

IEEE Trans. Ind. Electron.

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108

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The paper presents a fast, accurate and simple systematic approach for online condition monitoring and severity identification of ball bearings. This approach utilizes compact 1D 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 realtime processing ability and superior robustness against the highlevel noise presence, the compact and minimally-trained 1D CNNs in the core of the proposed approach can handle new damage scenarios with utmost accuracy.

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Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

Cited by 178 publications

References 43 publications

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

1-D Convolutional Neural Networks for Signal Processing Applications

Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring

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