Effective real-time monitoring of Tunnel Boring Machine (TBM) performance is hindered by complex vibrations arising from multi-scale dynamic interactions and nonlinear coupling effects. To address this, this study proposes a model-based framework for TBM vibration monitoring, which integrates a high-fidelity, multi-degree-of-freedom (MDOF) dynamic model with data from full-scale field experiments. The framework utilizes laboratory-derived cutter loading spectra as input for the dynamic model, while in-situ vibration measurements from a TBM operating at four distinct penetration rates (0–10.5 mm/r) serve for validation and analysis. Comparative frequency-domain analysis reveals that low-frequency vibrations (10–20 Hz) from cutter oscillations consistently align with the TBM's fundamental overturning modes (horizontal: 17.4 Hz, vertical: 19.2 Hz). Crucially, as penetration exceeds 5.8 mm/r, specific mid-to-high frequency bands (30–80 Hz) intensify, resulting from the modulation of axial translation modes by overturning frequencies. High-penetration regimes (>7.0 mm/r) generate distinct sideband clusters (e.g., f 9 + f 1 = 60.1 Hz) that dominate the vibration energy. This study quantifies the vibration transmission path from rock-breaking to cutterhead response, demonstrating that spectral signatures are intrinsically linked to operational parameters. The proposed framework provides a robust basis for developing advanced, data-driven diagnostic systems to monitor TBM-rock interaction and machine health. © 2026 Elsevier Ltd

Frequent drive unit failures in tunnel boring machines (TBMs) during hard rock tunneling, caused by extreme impact loads, necessitate accurate synchronization analysis to prevent motor overload and shaft torsion. However, existing models inadequately represent electromechanical coupling, limiting analysis accuracy. An electromechanical coupling model was developed to account for torque and load interactions. A condition monitoring system was designed and tailored to a specific type of TBM in a Chinese tunneling project, validating model predictions against in situ torque data. Analysis revealed a 21% maximum torque difference between motors, a critical imbalance that leads to accelerated fatigue in drive components and premature failures such as motor burnout and gear tooth fracture. As the driving gear shaft length increases, the chattering phenomenon in meshing vibrations becomes more pronounced. Longer gear shafts amplify meshing vibrations, with torsional vibration rms increasing by 36.19% as shaft length rises from 0.1 to 1 m. The pinion vibration contains characteristic frequencies ( f(n) ) including the system's natural frequencies (corresponding to different modes), meshing frequency ( f(m) ), pinion rotational frequency ( f(P) ), ring gear rotational frequency ( fR ), and combination frequencies ( f(n )+ f(m )+ kf(P )+ lf(R) , where k, l = 0 , +/- 1, +/- 2, & mldr;). Crucially, a sensitive cutterhead speed range of 2-4 r/min was identified as an "operational risk zone." Within this range, the rms load difference rises by 28.24%, providing actionable guidance to optimize tunneling parameters and enhance machine reliability.

针对多尺度问题分析时在两个异构的有限元网格接触界面存在单元结点不匹配而导致结点属性不能连续传递的问题,提出了多结点四边形单元结点形函数构建方法。首先将不规则四边形单元及其多结点通过等参逆变换转成规则正方形单元及其多结点,然后在规则单元中,以每个结点为基点,沿相互正交的两个方向在本单元内寻找近邻结点,以基点与近邻结点之间的距离和它们的属性变化值来建立该结点形函数的两个乘积因子,从而构建多结点形函数和修正原结点形函数,形函数将结点属性值的影响域限制在由基点和近邻结点所确定的四边形可控区域之内,实现了两接触网格结点属性在接触界面的无缝连接,从而保证了分析区域的场量变化的连续性、一致性和各向同性。

Visual surface defect detection, which aims to obtain the locations of defects and classify each defect into the corresponding category in a given image, is a critical task in an actual production process. Nowadays, more and more methods have made excellent progress in visual defect inspection. However, there still exist three tough challenges where these methods cannot handle well: large defect shape change, large-scale variation, and high-quality defect localization. In this paper, a Convolutional Neural Networks (CNN) based visual defect detection framework is proposed, which elegantly mitigated these three problems by introducing three well-designed components including deformable convolution module, balanced feature pyramid module and cascade head module. First, the feature maps contained with defect shape information are adaptively extracted by Resnet/ResneXt network with the deformable convolution operator. Then the balanced feature pyramid module is attached to the feature extraction module to obtain information-fused multilayer feature maps. Finally, the cascade head is applied to refine the predicted bounding box to achieve high-quality defect localization. Under the COCO evaluation metrics, our method significantly obtains 45.2 mAP with a large margin (4.9 AP) compared with Faster RCNN baseline.
© 2020 Elsevier Ltd

When the training data required by the data-driven model is insufficient or difficult to cover the sample space completely, incorporating the prior knowledge and prior knowledge compensation module into the support vector regression (PESVR) can significantly improve the accuracy and generalization performance of the model. However, the optimization problem to be solved is very complex, resulting long training time, and it must be retrained all the data from scratch every time the training set is modified. Comparing to standard support vector regression (SVR), PESVR has multiple input datasets and more complex objective function and constraints, including several coupling constraints, the existing methods cannot effectively solve accurate on-line learning of this nested (i.e. fully coupled) model. In this paper, an accurate on-line support vector regression incorporated with prior knowledge and error compensation is proposed. Under the constraint of Karush-Kuhn-Tucker conditions, the model parameters are updated recursively through the sequential adiabatic incremental adjustments. The error compensation model and the prediction model are updated simultaneously when a real measured sample or prior knowledge sample is added to or removed from the training set. The updated model is identical to the model produced by the batch learning algorithm. Experiments on an artificial dataset and several benchmark datasets show encouraged results for online learning and prediction.

Energy harvesting is an essential technology for enabling low-power, maintenance-free electronic devices, and thus has attracted much attention in recent years. In this paper, we propose a multi-material topology optimization approach for the design of energy harvesting piezoelectric composite structures. The energy conversion efficiency of piezoelectric composite structure is maximized by optimally distributing elastic, piezoelectric and void materials. To this end, a multi-material interpolation model is particularly established. In order to improve gradient-based mathematical programming algorithms, analytical sensitivities of topological design variables are derived using the adjoint method. An additional constraint on structural compliance is considered in design to maintain the load-carrying capability and improve the convergence. A variety of numerical experiments are performed to test our approach on a benchmark composite beam with piezoelectric layers. The proposed approach has been shown effective in increasing the energy conversion efficiency by the simultaneous distribution of the piezoelectric and non-piezoelectric materials. The performance calibration of the optimized design and the reconstructed topologies based on computer-aided design demonstrate the effectiveness of the proposed method under both static and harmonic load conditions.

Visual surface defect inspection for metal part has become a rapidly developing research field within the last decade. But due to the variances of defect shapes and scales, the inspection of tiny and irregular shape defects has posed challenges on the robustness of the inspection model. In this context, a deep learning method based on the deformable convolution and concatenate feature pyramid (CFP) neural networks is proposed to improve the inspection. We design a deformable convolution layer in the neural networks as an attention mechanism to adaptively extract the features of defect shape and location, which enhances the inspection of the defects with large shape variances. We also merge the multiple hierarchical features collected from different deformable convolution layers by the CFP, which improves the inspection of tiny defects. The results show that the proposed method has a better generalization ability than traditional convolution neural networks.
© 1963-2012 IEEE.

This article presents a design optimization framework which integrates realistic lightning strike electrostatic and fatigue analyses for designing reliable and economical composite wind turbine blades. The novel aspects of this work include: a parametric tortuous lightning stepped leader model that reflects one of the true natural characteristics of the lightning phenomenon; and characterization of both the lightning strike dielectric breakdown failure and multi-axial fatigue failure mechanisms for structural design of composite wind turbine blades. A case study of the structural design optimization of a 5 MW composite wind turbine blade is tested using the framework with two optimization solvers: sequential quadratic programming (SQP) and Bayesian optimization (BO). SQP produces a superior optimal design to BO. In the optimum blade design based on the SQP algorithm, the lightning safety ratio increased by 32% and the expected fatigue life increased more than 15 times compared with the initial blade design.
© 2019 Informa UK Limited, trading as Taylor & Francis Group.