A Review of Vibration Based Inverse Methods for Damage Detection and Identification in Mechanical Structures Using Optimization Algorithms and ANN

Gomes, Guilherme Ferreira; Mendéz, Yohan Alí Diaz; Alexandrino, Patrícia da Silva Lopes; Cunha, Sebastião Simões da; Ancelotti, Antônio Carlos

doi:10.1007/s11831-018-9273-4

Cited by 109 publications

(44 citation statements)

References 79 publications

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“…Therefore, one possible application for ML is condition monitoring and damage detection [65,66]. Most published work was related to vibration theory rather than tribology [67,68], which is why only some representative examples shall be introduced. As such, Subrahmanyam and Sujatha [69] investigated the suitability of two different ANNs, namely multilayered feed forward neural network trained with supervised error back propagation (EBP) technique and an unsupervised adaptive resonance theory-2 (ART2) based neural network, for the diagnosis of local defects in deep groove ball bearings.…”

Section: Rolling Bearingsmentioning

confidence: 99%

Current Trends and Applications of Machine Learning in Tribology—A Review

2021

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Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes. Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies.

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Section: Rolling Bearingsmentioning

confidence: 99%

Current Trends and Applications of Machine Learning in Tribology—A Review

2021

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“…Indeed, structural health monitoring (SHM) techniques are key methods in the risk-based safety assessment of such tower-line systems. In recent years, SHM techniques have been extended both from theoretical and applied aspects [5].…”

Section: Introductionmentioning

confidence: 99%

Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors

Mohammadi

Mirtaheri

Salkhordeh

et al. 2020

Sensors

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This study presents a technique to identify the vibration characteristics in power transmission towers and to detect the potential structural damages. This method is based on the curvature of the mode shapes coupled with a continuous wavelet transform. The elaborated numerical method is based on signal processing of the output that resulted from ambient vibration. This technique benefits from a limited number of sensors, which makes it a cost-effective approach compared to others. The optimal spatial location for these sensors is obtained by the minimization of the non-diagonal entries in the modal assurance criterion (MAC) matrix. The Hilbert–Huang transform was also used to identify the dynamic anatomy of the structure. In order to simulate the realistic condition of the measured structural response in the field condition, a 10% noise is added to the response of the numerical model. Four damage scenarios were considered, and the potential damages were identified using wavelet transform on the difference of mode shapes curvature in the intact and damaged towers. Results show a promising accuracy considering the small number of applied sensors. This study proposes a low-cost and feasible technique for structural health monitoring.

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“…Typical vibration-based parameters used for SHM consist of both indirectly measured data (such as resonant frequencies, modal flexibilities, mode shapes, modal strain energy, and modal stiffness) and directly measured data (such as timedomain data, frequency response functions, and power spectral densities) (Bagheri et al, 2017;Kong et al, 2017). Gomes et al (2019) provided a review on using vibration methods and advanced algorithms with the purpose of damage detection for mechanical structures using vibration analysis. The authors focused on the application of vibrationbased inverse problems and the development of this group of methods using computational intelligence tools, such as artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…The tools of signal processing for feature extraction are related to the Fourier transform (FT), short-time Fourier transform , Wigner-Ville distribution , wavelet transform and its variants (complex, continuous, discrete, packet, etc. ), and Hilbert-Huang transform (Babajanian Bisheh et al, 2019;Gomes et al, 2019;Jothi Saravanan et al, 2017;Kunwar et al, 2013;Pirboudaghi et al, 2018;Sun et al, 2020;Yang et al, 2017;Zhu et al, 2019). Li et al (2015) proposed a structural damage detection approach based on the power spectral density transmissibility (PSDT).…”

Section: Introductionmentioning

confidence: 99%

Damage detection of bridges based on combining efficient cepstral coefficients

Bisheh

Amiri

Nekooei

et al. 2020

Journal of Vibration and Control

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In this article, a novel vibration-based damage detection approach is proposed based on selecting effective cepstral coefficients, consisting of three main stages: (1) signal processing and feature extraction, (2) damage detection by combining effective cepstral coefficients through feature selection methods, and (3) performance evaluation. First, two feature extraction techniques are used in damage identification systems, including linear prediction cepstral coefficients and mel frequency cepstral coefficients. Second, to improve the performance of damage detection, the combination of the effective cepstral coefficients is proposed as a damage index. By applying several feature selection methods, the most effective coefficients are found and then combined to create a subset that carries the most significant information about the structural damage. Finally, the support vector machine classifier is performed to evaluate the proposed approach in detecting the structural damage. The proposed technique is verified using a suite of numerical and full-scale studies. Results confirm that the proposed method achieves a significant performance with great accuracy and reduces false alarms.

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A Review of Vibration Based Inverse Methods for Damage Detection and Identification in Mechanical Structures Using Optimization Algorithms and ANN

Cited by 109 publications

References 79 publications

Current Trends and Applications of Machine Learning in Tribology—A Review

Current Trends and Applications of Machine Learning in Tribology—A Review

Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors

Damage detection of bridges based on combining efficient cepstral coefficients

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