“…Moreover, for mechanistically unclear RUL prediction and degradation assessment issues, an extensive evaluation of potential models as loss function must be performed to ensure prediction performance of PIML. Figure 6Physics-informed DNN framework for structural damage identification [62]. …”
Section: Applications Of Piml In Structural Integritymentioning
confidence: 99%
“…Among them, Ma et al [72] emphasized the importance of building degradation models based on physical knowledge and ML. Huang et al [62] extracted physical features from the output of the finite-element model (FEM) as inputs to the NN and designed physical loss functions to evaluate the discrepancy between the prediction of NN and FEM in figure 6. The validity of the PIMLs were verified through numerical arithmetic and experimental studies.…”
Section: Applications Of Piml In Structural Integritymentioning
The development of machine learning (ML) provides a promising solution to guarantee the structural integrity of critical components during service period. However, considering the lack of respect for the underlying physical laws, the data hungry nature and poor extrapolation performance, the further application of pure data-driven methods in structural integrity is challenged. An emerging ML paradigm, physics-informed machine learning (PIML), attempts to overcome these limitations by embedding physical information into ML models. This paper discusses different ways of embedding physical information into ML and reviews the developments of PIML in structural integrity including failure mechanism modelling and prognostic and health management (PHM). The exploration of the application of PIML to structural integrity demonstrates the potential of PIML for improving consistency with prior knowledge, extrapolation performance, prediction accuracy, interpretability and computational efficiency and reducing dependence on training data. The analysis and findings of this work outline the limitations at this stage and provide some potential research direction of PIML to develop advanced PIML for ensuring structural integrity of engineering systems/facilities.
This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
“…Moreover, for mechanistically unclear RUL prediction and degradation assessment issues, an extensive evaluation of potential models as loss function must be performed to ensure prediction performance of PIML. Figure 6Physics-informed DNN framework for structural damage identification [62]. …”
Section: Applications Of Piml In Structural Integritymentioning
confidence: 99%
“…Among them, Ma et al [72] emphasized the importance of building degradation models based on physical knowledge and ML. Huang et al [62] extracted physical features from the output of the finite-element model (FEM) as inputs to the NN and designed physical loss functions to evaluate the discrepancy between the prediction of NN and FEM in figure 6. The validity of the PIMLs were verified through numerical arithmetic and experimental studies.…”
Section: Applications Of Piml In Structural Integritymentioning
The development of machine learning (ML) provides a promising solution to guarantee the structural integrity of critical components during service period. However, considering the lack of respect for the underlying physical laws, the data hungry nature and poor extrapolation performance, the further application of pure data-driven methods in structural integrity is challenged. An emerging ML paradigm, physics-informed machine learning (PIML), attempts to overcome these limitations by embedding physical information into ML models. This paper discusses different ways of embedding physical information into ML and reviews the developments of PIML in structural integrity including failure mechanism modelling and prognostic and health management (PHM). The exploration of the application of PIML to structural integrity demonstrates the potential of PIML for improving consistency with prior knowledge, extrapolation performance, prediction accuracy, interpretability and computational efficiency and reducing dependence on training data. The analysis and findings of this work outline the limitations at this stage and provide some potential research direction of PIML to develop advanced PIML for ensuring structural integrity of engineering systems/facilities.
This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
“…Liu et al integrated the forward operator model for magnetotelluric into the network training loop to reconstruct the subsurface resistivity model [24]. Huang et al incorporated the physics from the finite element mode into the network to guide the damage feature learning from measured data [25]. Guo et al proposed an iterative structure in which the forward modeling is embedded into the inversion neural network to solve full-wave inverse scattering problems (ISPs) in the 2D-case [26].…”
Pipeline corrosion quantification plays a vital role in guaranteeing the safety of critical industrial structures and thus significant work has been carried out to address such an issue. Although quantitative imaging is crucial for non-destructive testing, research in guided wave pipeline testing has primarily centered on qualitative approaches. Here, we propose a deep neural network built upon physical model to reconstruct pipe wall thickness from ultrasonic guided wave (UGW) signals. The workflow of reconstruction contains three layers, where each layer consists of a fixed forward network and a residual inversion network. The forward model is represented by an agent convolutional neural network which would be embedded into the entire inversion network. The residuals between data from the forward model and real signals are then mapped into velocity profile differences through sub-inversion network. Numerical experiments were conducted to verify the inversion performance of the deep neural network using thickness maps obtained from guided wave frequency domain information. Results show that inversion images are capable to reveal the positions, shapes, and depths of corrosion with high resolution and precision, yielding an average inversion of 87.37% in the test set. In addition, by utilizing the periodicity of the pipeline, the inversion accuracy of eight pairs of transducers were improved from 67.7% to 89.43% with high-order helical guided wave. Compared with traditional high-precision inversion methods such as full waveform inversion, the proposed method achieved approximately 300 times faster inversion speed at the cost of some accuracy. The research demonstrates that real-time quantitative imaging of defects on pipes can be achieved accurately by physics embedded network. Furthermore, an experimental verification of the method was carried out through UGW pipeline testing, demonstrating its feasibility. The mean squared error of wall thickness reconstruction was 0.0070, achieving a high level of precision.
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