Online fatigue crack prognosis using nonlinear ultrasonic modulation

Lim, Hyung Jin; Sohn, Hoon

doi:10.1177/1475921719828271

Cited by 28 publications

(10 citation statements)

References 36 publications

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“…The knowledge-based fault diagnosis method constructs a knowledge base that relies on domain expert experience and fault information and simulates the process of expert reasoning and matching with the algorithm to realize fault diagnosis. It is widely used in electric power [40], rail transit, and aviation [41]. Due to the parallel processing ability, graphical reasoning process and good fault tolerance of the incomplete fault information of Petri net, Sun [42], Li [43], Luo [44] and Xie [45] performed formal reasoning and modeling analysis and realized the diagnosis of grid fault through simulation experiments by using fuzzy Petri net, weighted fuzzy Petri net, directional weighted fuzzy Petri net and hierarchical fuzzy Petri net, respectively.…”

Section: ) Knowledge-based Methodsmentioning

confidence: 99%

Review of Key Technologies and Progress in Industrial Equipment Health Management

et al. 2020

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Industrial equipment health management is an extension of traditional fault diagnosis, which is a frontier research field of information science. The definition and application area of industrial equipment health management are first clarified, and then technologies related to health management are introduced, including feature analysis, fault diagnosis, fault prognosis and other technologies. The difficulties and bottlenecks of various technologies are expounded through comparative analysis, and a feasible scheme for future health management and integration is also discussed in this paper.

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Section: ) Knowledge-based Methodsmentioning

confidence: 99%

Review of Key Technologies and Progress in Industrial Equipment Health Management

et al. 2020

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“…Several non-destructive testing and structural health monitoring (SHM) approaches have been proposed to detect and monitor flaws in mechanical structures and offer a timely failure warning before flaws grow to critical sizes [1][2][3][4]. Recently, guided ultrasonic wave (GUW)-based SHM approaches [5][6][7][8][9][10][11][12][13] have attracted intensive attention due to the superb merits of GUWs, including long-range gauging capacity, high sensitivity to small-scale damage, ability to detect damage at inaccessible locations and cost-effectiveness in implementation. In general, investigation on damage detection using GUWs can be divided into three categories: physicalmodel-based analytical interpretation, numerical simulation and experimental implementation.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, GUW-based damage detection has been realized by performing numerical simulation using finite element method [20][21][22][23][24], spectral element method [25][26][27] and boundary element method [28,29]. Experimental validation of GUW-based damage evaluation has also been realized in both metal and composite materials [7,12,19,[30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Wavelet packet energy-based damage detection using guided ultrasonic waves and support vector machine

Lyu

Yang

et al. 2023

Meas. Sci. Technol.

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Substantial work has shown that the modulation of structural damage on probing guided ultrasonic waves (GUWs) can result in wave components corresponding to different frequencies, causing wave energy transfer from central frequency to other frequency bands. To make use of the damage-induced wave energy transfer in different frequency bands, a data-driven method which combines wavelet packet decomposition (WPD), principal component analysis (PCA) and support vector machine (SVM), is proposed in this study for structural damage identification in both metallic and composite materials. Firstly, WPD is employed to decompose the original signal into different frequency bands, based on which the wave energy at each frequency band can be determined. A wave energy distribution vector is constructed according to the energy proportion of each frequency band. Then, PCA is recalled conducting dimensionality reduction for the energy distribution vectors, in order to improve the computational efficiency for subsequent SVM classification. The compressed energy distribution vectors are used as the input to train an SVM-based classifier for identifying structural damage. To validate the proposed WPD-PCA-SVM method, experiments are performed on both aluminium plate and glass fibre reinforced polymer (GFRP) laminate. According to the experimental results, the embryonic fatigue crack in the metal plate and the anomaly in the GFRP laminate can be identified by the proposed method, with a detection accuracy of 92.86% for aluminium plate and 95.45% for GFRP laminate, respectively, demonstrating the effectiveness of the proposed method for damage detection in both metallic and composite materials.

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“…The second order nonlinearity parameter with two fundamental frequencies was used to detect microcracks [13]. Lim et al developed a technique with the frequency modulation of high-frequency and low-frequency waves [14]. Subharmonic frequencies were used to detect closed cracks [15].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Ultrasonic Modulation Method for Crack Detection in Turbine Blades

Mevissen

Meo

2020

Aerospace

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In modern gas turbines, efforts are being made to improve efficiency even further. This is achieved primarily by increasing the generated pressure ratio in the compressor and by increasing the turbine inlet temperature. This leads to enormous loads on the components in the hot gas region in the turbine. As a result, non-destructive testing and structural health monitoring (SHM) processes are becoming increasingly important to gas turbine manufacturers. Initial cracks in the turbine blades must be identified before catastrophic events occur. A proven method is the linear ultrasound method. By monitoring the amplitude and phase fluctuations of the input signal, structural integrity of the components can be detected. However, closed cracks or small cracks cannot be easily detected due to a low impedance mismatch with the surrounding materials. By contrast, nonlinear ultrasound methods have shown that damages can be identified at an early stage by monitoring new signal components such as sub- and higher harmonics of the fundamental frequency in the frequency spectrum. These are generated by distortion of the elastic waveform due to damage/nonlinearity of the material. In this paper, new global nonlinear parameters were derived that result from the dual excitation of two different ultrasound frequencies. These nonlinear features were used to assess the presence of cracks as well as their qualitative sizes. The proposed approach was tested on several samples and turbine blades with artificial and real defects. The results were compared to samples without failure. Numerical simulations were conducted to investigate nonlinear elastic interaction of the stress waves with the damage regions. The results show a clear trend of nonlinear parameters changing as a function of the crack size, demonstrating the capability of the proposed approach to detect in-service cracks.

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Online fatigue crack prognosis using nonlinear ultrasonic modulation

Cited by 28 publications

References 36 publications

Review of Key Technologies and Progress in Industrial Equipment Health Management

Review of Key Technologies and Progress in Industrial Equipment Health Management

Wavelet packet energy-based damage detection using guided ultrasonic waves and support vector machine

A Nonlinear Ultrasonic Modulation Method for Crack Detection in Turbine Blades

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