•Applications of machine learning to machine fault diagnosis are reviewed.•Traditional machine learning brought intelligence to fault diagnosis in the past.•Deep learning focuses on further enhanced ...benefits in the present.•Transfer learning promotes achievements to engineering scenarios in the future.•A roadmap of intelligent fault diagnosis is pictured to provide research trends.
Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to machine fault diagnosis. This is a promising way to release the contribution from human labor and automatically recognize the health states of machines, thus it has attracted much attention in the last two or three decades. Although IFD has achieved a considerable number of successes, a review still leaves a blank space to systematically cover the development of IFD from the cradle to the bloom, and rarely provides potential guidelines for the future development. To bridge the gap, this article presents a review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective. In the past, traditional machine learning theories began to weak the contribution of human labor and brought the era of artificial intelligence to machine fault diagnosis. Over the recent years, the advent of deep learning theories has reformed IFD in further releasing the artificial assistance since the 2010s, which encourages to construct an end-to-end diagnosis procedure. It means to directly bridge the relationship between the increasingly-grown monitoring data and the health states of machines. In the future, transfer learning theories attempt to use the diagnosis knowledge from one or multiple diagnosis tasks to other related ones, which prospectively overcomes the obstacles in applications of IFD to engineering scenarios. Finally, the roadmap of IFD is pictured to show potential research trends when combined with the challenges in this field.
As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image ...segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbors and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper. First, the local spatial information of images is incorporated into FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Second, the modification of membership partition, based on the distance between pixels within local spatial neighbors and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of membership partition. Compared with state-of-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster, since it is unnecessary to compute the distance between pixels within local spatial neighbors and clustering centers. In addition, it is efficient for noisy image segmentation because membership filtering are able to improve membership partition matrix efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.
Deep convolutional neural networks (CNNs) have achieved much success in remote sensing image change detection (CD) but still suffer from two main problems. First, the existing multiscale feature ...fusion methods often use redundant feature extraction and fusion strategies, which often lead to high computational costs and memory usage. Second, the regular attention mechanism in CD is difficult to model spatial-spectral features and generate 3-D attention weights at the same time, ignoring the cooperation between spatial features and spectral features. To address the above issues, an efficient ultralightweight spatial-spectral feature cooperation network (USSFC-Net) is proposed for CD in this article. The proposed USSFC-Net has two main advantages. First, a multiscale decoupled convolution (MSDConv) is designed, which is clearly different from the popular atrous spatial pyramid pooling (ASPP) module and its variants since it can flexibly capture the multiscale features of changed objects using cyclic multiscale convolution. Meanwhile, the design of MSDConv can greatly reduce the number of parameters and computational redundancy. Second, an efficient spatial-spectral feature cooperation (SSFC) strategy is introduced to obtain richer features. The SSFC differs from the existing 2-D attention mechanisms since it learns 3-D spatial-spectral attention weights without adding any parameters. The experiments on three datasets for remote sensing image CD demonstrate that the proposed USSFC-Net achieves better CD accuracy than most CNNs-based methods and requires lower computational costs and fewer parameters, even it is superior to some Transformer-based methods. The code is available at https://github.com/SUST-reynole/USSFC-Net .
Most of the approaches used for Landslide inventory mapping (LIM) rely on traditional feature extraction and unsupervised classification algorithms. However, it is difficult to use these approaches ...to detect landslide areas because of the complexity and spatial uncertainty of landslides. In this letter, we propose a novel approach based on a fully convolutional network within pyramid pooling (FCN-PP) for LIM. The proposed approach has three advantages. First, this approach is automatic and insensitive to noise because multivariate morphological reconstruction is used for image preprocessing. Second, it is able to take into account features from multiple convolutional layers and explore efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of context. Finally, the selected PP module addresses the drawback of global pooling employed by convolutional neural network, FCN, and U-Net, and, thus, provides better feature maps for landslide areas. Experimental results show that the proposed FCN-PP is effective for LIM, and it outperforms the state-of-the-art approaches in terms of five metrics, <inline-formula> <tex-math notation="LaTeX">Precision </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">Recall </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">Overall~Error </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">F </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">score </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">Accuracy </tex-math></inline-formula>.
Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card ...data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.
Popular semi-supervised medical image segmentation networks often suffer from error supervision from unlabeled data since they usually use consistency learning under different data perturbations to ...regularize model training. These networks ignore the relationship between labeled and unlabeled data, and only compute single pixel-level consistency leading to uncertain prediction results. Besides, these networks often require a large number of parameters since their backbone networks are designed depending on supervised image segmentation tasks. Moreover, these networks often face a high over-fitting risk since a small number of training samples are popular for semi-supervised image segmentation. To address the above problems, in this paper, we propose a novel adversarial self-ensembling network using dynamic convolution (ASE-Net) for semi-supervised medical image segmentation. First, we use an adversarial consistency training strategy (ACTS) that employs two discriminators based on consistency learning to obtain prior relationships between labeled and unlabeled data. The ACTS can simultaneously compute pixel-level and image-level consistency of unlabeled data under different data perturbations to improve the prediction quality of labels. Second, we design a dynamic convolution-based bidirectional attention component (DyBAC) that can be embedded in any segmentation network, aiming at adaptively adjusting the weights of ASE-Net based on the structural information of input samples. This component effectively improves the feature representation ability of ASE-Net and reduces the overfitting risk of the network. The proposed ASE-Net has been extensively tested on three publicly available datasets, and experiments indicate that ASE-Net is superior to state-of-the-art networks, and reduces computational costs and memory overhead. The code is available at: https://github.com/SUST-reynole/ASE-Nethttps://github.com/SUST-reynole/ASE-Net .
The popular Siamese convolutional neural networks (CNNs) for remote sensing (RS) image change detection (CD) often suffer from two problems. First, they either ignore the original information of ...bitemporal images or insufficiently utilize the difference information between bitemporal images, which leads to the low tightness of the changed objects. Second, Siamese CNNs always employ dual-branch encoders for CD, which increases computational cost. To address the above issues, this article proposes a network based on difference enhancement and spatial-spectral nonlocal (DESSN) for CD in very-high-resolution (VHR) images. This article makes threefold contributions. First, we design a difference enhancement (DE) module that can effectively learn the difference representation between foreground and background to reduce the impact of irrelevant changes on the detection results. Second, we present a spatial-spectral nonlocal (SSN) module that is different from vanilla nonlocal because multiscale spatial global features are incorporated to model the large-scale variation of objects during CD. The module can be used to strengthen the edge integrity and internal tightness of changed objects. Third, the asymmetric double convolution with Ghost (ADCG) module is exploited instead of standard convolution. The ADCG can not only refine the edge information of the changed objects, since horizontal and vertical convolutional kernels have good contour preservation advantages, but also greatly reduce the computational complexity of the proposed model. The experiments on two public VHR CD datasets demonstrate that the proposed network can provide higher detection accuracy and requires smaller memory usage than state-of-the-art networks.
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey ...on medical image segmentation using deep learning techniques is presented. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi‐level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyse literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.
It is extremely common in engineering to design algorithms to perform various tasks. In data-driven decision making in any field one needs to ascertain the quality of an algorithm. Therefore, a ...robust assessment of algorithms is essential in deciding the best algorithm as well as improving algorithms. To perform such an assessment objectively is obvious in the case of a single performance metric, but it is unclear in the case of multiple metrics. Nonetheless, F 1 measure is widely used in cases with two metrics; F 1 measure represents the harmonic mean ( HM ) of two metrics. Of course, there are other means, e.g., the arithmetic mean ( AM ) and the geometric mean ( GM ). As motivations for using them are intuitive and none of them are based on any objective function, it is difficult to judge objectively which is the best one. In this paper, the single metric case is examined to develop two objective functions that are applicable for any number of metrics. These two objective functions lead to two different performance measures - the distance from the origin ( DO ) and the distance from the ideal position ( DIP ). It introduces a new concept of the remaining phase space for the evaluation of the quality of a performance measure. On further and closer examinations of the original goal and the phase space of the metrics, amongst these five measures, either HM or DIP is found to be the best. Specifically, it is found that HM is the best measure at the lower performance end, while DIP is clearly the best measure at the higher performance end, of much practical interest. Moreover, rules for deciding the best algorithm and the order of a set of algorithms are presented. These results are derived in the context of multiple independent and bounded metrics. Furthermore, several properties and detailed discussions are provided, following which some published results are reviewed in the present context to elucidate some points.
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