The intelligent fault diagnosis powered deep learning (DL) is widely applied in various practical industries, but the conventional intelligent fault diagnosis methods cannot fully juggle the manifold ...structure information with multiple-order similarity from the massive unlabeled industrial data. Thus, a new Multiple-Order Graphical Deep Extreme Learning Machine (MGDELM) algorithm for unsupervised fault diagnosis (UFD) of rolling bearing is proposed in this study. Specifically, the developed MGDELM algorithm mainly contains two parts: 1) one is unsupervised multiple-order feature extraction, the first-order proximity with Cauchy graph embedded is applied to extract the local structural information, and the second-order proximity is simultaneously employed for mining global structural information and 2) the other used is the unsupervised Fuzzy-C-Mean (FCM) into fault clustering built on the extracted multiple-order graph embedding features. Empirically, two cases of rolling bearing failure data validate the effectiveness of the proposed algorithm and fault diagnosis method. By jointly optimizing the multiple-order objective function, the proposed MGDELM algorithm can synchronously extract local and global structural information from the raw industrial data. This study also provides a novel promising approach for UFD.
The integrity of geomagnetic data is a critical factor in understanding the evolutionary process of Earth's magnetic field, as it provides useful information for near-surface exploration, unexploded ...explosive ordnance detection, and so on. Aimed to reconstruct undersampled geomagnetic data, this paper presents a geomagnetic data reconstruction approach based on machine learning techniques. The traditional linear interpolation approaches are prone to time inefficiency and high labor cost, while the proposed approach has a significant improvement. In this paper, three classic machine learning models, support vector machine, random forests, and gradient boosting were built. Besides, a deep learning algorithm, recurrent neural network, was explored to further improve the training performance. The proposed learning models were used to specify a continuous regression hyperplane from a training data. The specified regression hyperplane is a mapping of the relation between the mock-up missing data and the surrounding intact data. Afterward, the trained models, essentially the hyperplanes, were used to reconstruct the missing geomagnetic traces for validation, and they can be used for reconstructing further collected new field data. Finally, numerical experiments were derived. The results showed that the performance of our methods was more competitive in comparison with the traditional linear method, as the reconstruction accuracy was increased by approximately 10%~20%.
Detection of the liquefied natural gas (LNG) leakage attracts increasing attention for preventing environments and governments from severe pollution and economic loss. Existing frameworks take ...advantage of stationary surveillance thermal cameras to detect the LNG leakage, which comprises background subtraction and leakage classification. However, these methods are limited in rural areas due to the lack of sensitivity and accuracy. In this article, a generalized framework, i.e., tensor-based leakage detection (TBLD), is proposed to detect LNG leakage in the rural area from surveillance thermal cameras. First, the proposed TBLD takes advantage of tensor factorization to fuse thermal image and corresponding gradient maps for improving sensitivity. Additionally, a finite-state-machine is designed to maintain leakage foreground along with the video streaming. The experiments demonstrate the robust performance of TBLD in the background subtraction stage. Second, multiple classification techniques are explored in the leakage classification stage. The results suggest that the TBLD can accurately detect the LNG leakage by applying 50 layers of residual networks (ResNet50). Finally, compared with contemporary frameworks, the TBLD has consistently improved performance concerning the different distances of LNG leakage. The experimental results demonstrate the effectiveness of the proposed TBLD, which also shows the great potential of TBLD in future industrial applications.
Surface defects directly affect the mechanical properties of industrial strip steel products. To evaluate the integrity of the strip steel surface, a channel-wise global Transformer-based dual-branch ...network (CGTD-Net) for strip steel surface defect detection, dubbed CGTD-Net, is proposed in this study. First, the strip steel surface images are preprocessed using saturation adjustment and random flipping strategies to remove unnecessary background information and improve network generalization. Second, the Swin Transformer is employed at the end of the backbone network and the negative impacts of a single channel are then mitigated by using the multichannel feature pyramid networks via Transformer, which improves the extraction ability of the global semantic information for tiny or narrow defects. Third, an edge detection branch network is constructed with a spatial-channel global attention (SCGA) module to further enhance the feature extraction on both spatial and channel information. Finally, the CGTD-Net is compared with 11 state-of-the-art methods on the NEU-DET dataset, and ablation experiments are also implemented. The comparison results, conducted on a single 3090Ti GPU, reveal that the CGTD-Net achieves a mean intersection over union (mIoU) of 75.16% at 178 frames/s, outperforming other methods. The ablation experiment demonstrates that the CGTD-Net improves the mIoU by 7.83% and the <inline-formula> <tex-math notation="LaTeX">{F} </tex-math></inline-formula>-score by 6.3% compared to the baseline.
The geographical presentation of a house, which refers to the sightseeing and topography near the house, is a critical factor to a house buyer. The street map is a type of common data in our daily ...life, which contains natural geographical presentation. This paper sources real estate data and corresponding street maps of houses in the city of Los Angeles. In the case study, we proposed an innovative method, attention-based multi-modal fusion, to incorporate the geographical presentation from street maps into the real estate appraisal model with a deep neural network. We firstly combine the house attribute features and street map imagery features by applying the attention-based neural network. After that, we apply boosted regression trees to estimate the house price from the fused features. This work explored the potential of attention mechanism and data fusion in the applications of real estate appraisal. The experimental results indicate the competitiveness of proposed method among state-of-the-art methods.
Geomagnetic data forecasting plays a critical role for natural disaster institutions to respond promptly or make decisions for the magnetic storm warning, earthquake early warning, and so on. ...However, the forecasting accuracy is not always reliable due to the spatial correlations among different sites and the temporal correlations from each site measurement update. To address the correlation problems, a novel sparse geomagnetic data forecasting (SGCast) matrix framework is proposed in this study. To be specific, a coupled matrix factorization is proposed to model the sparse multilocation geomagnetic data and spatial correlation, which is demonstrated with an example from seven Chinese cities. After the factorization, two subspaces, location subspace, and temporal subspace are derived. Finally, the future geomagnetic signals are reconstructed based on forecasting temporal subspace and matrix reconstruction. The experimental results from the extensive comparison studies demonstrate the superiority of the proposed SGCast approach compared to the state-of-the-art approaches, with an approximate improvement of the forecasting accuracy as 10%~15%.
Efficient and robust search and rescue actions are always required when natural or technical disasters occur. Empowered by remote sensing techniques, building damage assessment can be achieved by ...fusing aerial images of pre- and post-disaster environments through computational models. Existing methods pay over-attention to assessment accuracy without considering model efficiency and uncertainty quantification in such a life-critical application. Thus, this article proposes an efficient and uncertain-aware decision support system (EUDSS) that evolves the recent computational models into an efficient decision support system, realizing the uncertainty during building damage assessment (BDA). Specifically, a new efficient and uncertain-aware BDA integrates the recent advances in computational models such as Fourier attention and Monte Carlo Dropout for uncertainty quantification efficiently. Meanwhile, a robust operation (RO) procedure is designed to invite experts for manual reviews if the uncertainty is high due to external factors such as cloud clutter and poor illumination. This procedure can prevent rescue teams from missing damaged houses during operations. The effectiveness of the proposed system is demonstrated on a public dataset from both quantitative and qualitative perspectives. The solution won the first place award in International Overhead Imagery Hackathon.
A leak detection and repair survey (LDAR) is essential to ensure a reliable and safe liquefied natural gas (LNG) supply. Modern LDAR systems deploy numerous fixed thermal imaging devices to ...automatically monitor the risk of potential leaks empowered by computational intelligence frameworks. Existing frameworks employ either background subtraction-based (BGS-based) or deep neural network-based (DNN-based) frameworks for LNG leak detection from thermal images. However, the BGS-based frameworks feature high sensitivity to perceive LNG emissions with low precision. On the contrary, the DNN-based frameworks can precisely classify the LNG leak after training while the sensitivity is low. Additionally, conventional DNN-based frameworks are difficult in modeling non-rigid objects such as LNG gas due to limited perceptive fields. Therefore, this study proposes a hybrid framework, namely foreground fusion-based gas detection (FFBGD), combining the advantages of BGS-based and DNN-based detectors for improved detection robustness through newly introduced concept of information fusion to LNG industries. Specifically, a foreground fusion network (FFN) is designed to fuse information of original thermal and foreground images after BGS based on the visual attention mechanism. Meanwhile, several advanced modules, i.e. deformable convolution, feature pyramid network, and cascade region-of-interest (ROI) head are adopted to enhance leak detection by offering better perceptive fields. Extensive experiments are carried out in this study to demonstrate the significant improvement brought by the proposed FFBGD over leak detection accuracy and robustness. Hence, the proposed solution can be deployed in energy facilities and enable reliable visual surveillance of LNG leaks.
In-service bridge structural performance analysis and prediction are usually complicated and challenging because of many unknown and uncertain factors. Contrary to the traditional structural ...appearance inspections and load tests, structural health monitoring (SHM) can provide a perspective for online analysis, prediction, and early warning. So far, SHM has been widely used in many bridge structures, and a lot of bridge SHM data have also been collected. However, the existing studies usually focus on some independent and unsystematic analysis methods, which are hard to use widely in engineering applications to reveal the overall structural performance. This study focuses on the structural performance analysis and prediction of the highway in-service bridge. The dynamic problems in bridge SHM are pointed out firstly, followed by a detailed analysis about the characteristics of bridge SHM data. With the consideration of different characteristics, three targeted analysis methods are proposed. An urban concrete-filled steel tube (CFST) truss girder bridge (opened to traffic in 1995) is also presented, which once experienced some prominent vibration problems. The bridge SHM system is designed and stalled after several appearance inspections, load tests, and some reinforcement measures. The data mining methods proposed (distribution function, association analysis, and time-series analysis) are employed for the analysis and prediction of structural response and deterioration extent. This study can provide some references for maintenance and management and can also build a foundation for further online analysis and early warning.
Concealed metallic object detection is one of the critical tasks for any security system. It has been proved that different objects have their own magnetic fingerprints, which are a series of ...magnetic anomalies determined by shape, size, physical composition, etc. This study addresses the design of a low-cost power security system for the detection of metallic objects according to their response to the magnetic field. The system consists of three anisotropic magnetoresistance (AMR) sensor arrays, detection circuits, and a microcontroller. A magnetic gradient full-tensor configuration, utilizing four AMR sensors arranged on a planar cross structure, was employed to construct a two-dimensional image from the obtained data, which can further suppress the background noise and reduce the orientation and orthogonality errors. The performance of the system is validated by data validation and multiple object feature segmentation. Numerous magnetic fingerprinting results demonstrate that the system can configure metallic objects more than 50cm clearly and identify multiple objects separated by less than 20 cm, which indicates the feasibility of using this magnetic gradient tensor fingerprint method for metallic object detection.
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