Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion

Tao, Hongfeng; Qiu, Jier; Chen, Yiyang; Stojanović, Vladimir; Cheng, Long

doi:10.1016/j.jfranklin.2022.11.004

Cited by 142 publications

(20 citation statements)

References 33 publications

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“…In addition, compared with traditional ACMs requiring iterations to solve PDEs, the employment of CNN greatly reduces computation cost in image segmentation, although its training process is generally long. In addition, later researchers embed some loss functions in deep learning [76][77][78][79][80][81] in region-based level set energy functions to improve segmentation efficiency and accuracy. Therefore, one can put the energy function of diverse ACMs mentioned in this paper and other segmentation models in deep learning together to design some new hybrid energy functions to further improve segmentation performance, which is recommended as a promising future research direction in the area of image segmentation.…”

Section: The Combination Of Deep Learning Modelsmentioning

confidence: 99%

An overview of intelligent image segmentation using active contour models

Chen¹,

Ge²,

Wang³

et al. 2023

Intell Robot

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The active contour model (ACM) approach in image segmentation is regarded as a research hotspot in the area of computer vision, which is widely applied in different kinds of applications in practice, such as medical image processing. The essence of ACM is to make use ofuse an enclosed and smooth curve to signify the target boundary, which is usually accomplished by minimizing the associated energy function by means ofthrough the standard descent method. This paper presents an overview of ACMs for handling image segmentation problems in various fields. It begins with an introduction briefly reviewing different ACMs with their pros and cons. Then, some basic knowledge in of the theory of ACMs is explained, and several popular ACMs in terms of three categories, including region-based ACMs, edge-based ACMs, and hybrid ACMs, are detailedly reviewed with their advantages and disadvantages. After that, twelve ACMs are chosen from the literature to conduct three sets of segmentation experiments to segment different kinds of images, and compare the segmentation efficiency and accuracy with different methods. Next, two deep learning-based algorithms are implemented to segment different types of images to compare segmentation results with several ACMs. Experimental results confirm some useful conclusions about their sharing strengths and weaknesses. Lastly, this paper points out some promising research directions that need to be further studied in the future.

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Section: The Combination Of Deep Learning Modelsmentioning

confidence: 99%

An overview of intelligent image segmentation using active contour models

Chen¹,

Ge²,

Wang³

et al. 2023

Intell Robot

View full text Add to dashboard Cite

show abstract

“…Bearings not only serve as crucial components in rotating equipment but also represent vulnerable parts that are prone to damage. The health of bearings plays a very important role in the normal operation of the entire mechanical equipment [1,2]. Consequently, extensive research on bearing fault diagnosis has emerged as a prominent area of interest among researchers [3].…”

Section: Introductionmentioning

confidence: 99%

Research on bearing ZC feature selection method based on DWCMI

Chen,

Liu,

2024

Meas. Sci. Technol.

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In light of the issues related to the omission of crucial features and the incorrect selection of redundant features in existing feature selection methods for zero-crossing (ZC) features, this paper presents a feature selection method based on Dynamic Weights Condition Mutual Information (DWCMI). In this method, the main factor of the objective function for feature evaluation is conditional mutual information, while also incorporating a complementary evaluation criterion based on conditional mutual information to address the issue of misselecting redundant features. By introducing a dynamic weight coefficient, we can accurately measure the importance of selected features by assessing their dynamic change in mutual information, thereby avoiding any oversight of crucial features. In the process of designing the algorithm, computational efficiency is improved by buffering and reusing previously calculated mutual information. This approach avoids the issue of repeatedly calculating the mutual information. The necessity, effectiveness, and high efficiency of the DWCMI method have been verified through simulation and experimentation.

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“…More recently, unsupervised methods, which do not require labeled data during training, have been shown effective at addressing anomaly and fault detection problems in various data settings. Tao et al [10] introduce an unsupervised cross-domain diagnosis method to learn fault features specific to the target domain using unlabeled data from the source domain. Song et al [11] present an adaptive neural finite-time resilient dynamic surface control strategy to overcome unknown control coefficients induced by severe faults and false data injection attacks.…”

Section: Introductionmentioning

confidence: 99%

A graph encoder–decoder network for unsupervised anomaly detection

Mesgaran,

Hamza

2023

Neural Comput & Applic

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A key component of many graph neural networks (GNNs) is the pooling operation, which seeks to reduce the size of a graph while preserving important structural information. However, most existing graph pooling strategies rely on an assignment matrix obtained by employing a GNN layer, which is characterized by trainable parameters, often leading to significant computational complexity and a lack of interpretability in the pooling process. In this paper, we propose an unsupervised graph encoder-decoder model to detect abnormal nodes from graphs by learning an anomaly scoring function to rank nodes based on their degree of abnormality. In the encoding stage, we design a novel pooling mechanism, named LCPool, which leverages locality-constrained linear coding for feature encoding to find a cluster assignment matrix by solving a least-squares optimization problem with a locality regularization term. By enforcing locality constraints during the coding process, LCPool is designed to be free from learnable parameters, capable of efficiently handling large graphs, and can effectively generate a coarser graph representation while retaining the most significant structural characteristics of the graph. In the decoding stage, we propose an unpooling operation, called LCUnpool, to reconstruct both the structure and nodal features of the original graph. We conduct empirical evaluations of our method on six benchmark datasets using several evaluation metrics, and the results demonstrate its superiority over state-of-the-art anomaly detection approaches.

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Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion

Cited by 142 publications

References 33 publications

An overview of intelligent image segmentation using active contour models

An overview of intelligent image segmentation using active contour models

Research on bearing ZC feature selection method based on DWCMI

A graph encoder–decoder network for unsupervised anomaly detection

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