Improving the robustness of spatial networks by link addition: more and dispersed links perform better

Dong, Zhengcheng; Tian, Meng; Tang, Ruoli; Li, Xin; Lai, Jingang

doi:10.1007/s11071-020-05607-5

Cited by 10 publications

(5 citation statements)

References 52 publications

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“…where dF is the difference between any two adjacent HI points. e closer Mon is to 1, the better the performance is [41][42][43][44][45][46][47][48][49][50][51][52][53]. We take bearing 12 as an example; the Mon result of different models is shown in Table 2.…”

Section: E Presented Methods Compared With She Ae and Pementioning

confidence: 99%

Data-Driven Bearing Fault Diagnosis of Microgrid Network Power Device Based on a Stacked Denoising Autoencoder in Deep Learning and Clustering by Fast Search without Data Labels

Shu

et al. 2020

Complexity

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The traditional health indicator (HI) construction method of electric equipment devices in microgrid networks, such as bearings that require different time-frequency domain indicators, needs several models to combine. Therefore, it is necessary to manually select appropriate and sensitive models, such as time-frequency domain indicators and multimodel fusion, to build HIs in multiple steps, which is more complicated because sensitivity characteristics and suitable models are more representatives of bearing degradation trends. In this paper, we use the stacked denoising autoencoder (SDAE) model in deep learning to construct HI directly from the microgrid power equipment of raw signals in bearings. With this model, the HI can be constructed without multiple model combinations or the need for manual experience in selecting the sensitive indicators. The SDAE can extract the representative degradation information adaptively from the original data through several nonlinear hidden layers automatically and approximate complicated nonlinear functions with a small reconstruction error. After the SDAE extracts the preliminary HI, a model is needed to divide the wear state of the HI constructed by the SDAE. A cluster model is commonly used for this, and unlike most clustering methods such as k-means, k-medoids, and fuzzy c-means (FCM), in which the clustering center point must be preset, cluster by fast search (CFS) can automatically find available cluster center points automatically according to the distance and local density between each point and its clustering center point. Thus, the selected cluster center points are used to divide the wear state of the bearing. The root mean square (RMS), kurtosis, Shannon entropy (SHE), approximate entropy (AE), permutation entropy (PE), and principal component analysis (PCA) are also used to construct the HI. Finally, the results show that the performance of the method (SDAE-CFS) presented is superior to other combination HI models, such as EEMD-SVD-FCM/k-means/k-medoids, stacked autoencoder-CFS (SAE-CFS), RMS, kurtosis, SHE, AE, PE, and PCA.

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Section: E Presented Methods Compared With She Ae and Pementioning

confidence: 99%

Data-Driven Bearing Fault Diagnosis of Microgrid Network Power Device Based on a Stacked Denoising Autoencoder in Deep Learning and Clustering by Fast Search without Data Labels

Shu

et al. 2020

Complexity

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“…For the DBN hidden layer network structure, research 11,12,[33][34][35][36][37][38][39] has shown that using triangle-based structures can extract fault features effectively and reduce data dimensionality at the same time. Here, the triangle structure represents the number of neuron nodes in the next RBM as half of the preceding RBM.…”

Section: Methodsmentioning

confidence: 99%

Health indicator construction for roller bearing based on an unsupervised deep belief network with a novel sigmoid zero local minimum point model

Shu

et al. 2020

Structural Health Monitoring

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Extracting bearing degradation curves with good smoothness and monotonicity as a health indicator lays a solid foundation for predicting the bearing’s remaining useful life. Traditional bearing health indicator construction methods generally have the following problems: (1) they require manual experience, such as manual labeling of data is burdensome when the amount of collected data is large, for feature extraction, selection, and fusion with other indicators and models because the methods rely on substantial expert experience and signal-processing technology; (2) deep belief networks in deep learning require engineering experts with rich experience to label the data, and because the degradation state of a bearing is constantly changing, it is difficult to rely on manual experience to distinguish and label it accurately; (3) owing to the noise in the data collected during the study, the extracted health indicator curve shows obvious oscillation and poor smoothness. In response to the above problems, this study proposes a model based on an unsupervised deep belief network and a new sigmoid zero local minimum point to eliminate health indicator curve oscillation and improve monotonicity. The main idea is that a deep belief network without a label output layer is used to extract the preliminary health indicator curve directly from the original signal, whereas the sigmoid zero local minimum point uses the average value based on a sigmoid function to reduce the weight of the current health indicator value to eliminate concussion, and then it uses the zero and local minimum points to further improve the monotonicity of the extracted health indicator without parameters. Finally, the superiority of the model proposed in this study (deep belief network–sigmoid zero local minimum point) is verified through a comparison of multiple bearing datasets and other models.

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“…In networks with more complicated structures, link addition methods have also been discussed [ 21 – 23 ]. For improving the robustness of both interdependent and multiplex networks, the minimum degree strategy is effective [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…The minimum degree strategy is considered an effective method for interdependent and multiplex networks, while the longest distance strategy is effective for spatial networks. In spatial networks, adding many links or long links can improve robustness against attacks [ 23 ]. Although there are several discussions about effective link addition methods in various types of networks, they are basically based on either the minimum degree or longest distance strategies.…”

Section: Introductionmentioning

confidence: 99%

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Adding links on minimum degree and longest distance strategies for improving network robustness and efficiency

Chujyo

Hayashi²

2022

PLoS ONE

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Many real-world networks characterized by power-law degree distributions are extremely vulnerable against malicious attacks. Therefore, it is important to obtain effective methods for strengthening the robustness of the existing networks. Previous studies have been discussed some link addition methods for improving the robustness. In particular, two effective strategies for selecting nodes to add links have been proposed: the minimum degree and longest distance strategies. However, it is unclear whether the effects of these strategies on the robustness are independent or not. In this paper, we investigate the contributions of these strategies to improving the robustness by adding links in distinguishing the effects of degrees and distances as much as possible. Through numerical simulation, we find that the robustness is effectively improved by adding links on the minimum degree strategy for both synthetic trees and real networks. As an exception, only when the number of added links is small, the longest distance strategy is the best. Conversely, the robustness is only slightly improved by adding links on the shortest distance strategy in many cases, even combined with the minimum degree strategy. Therefore, enhancing global loops is essential for improving the robustness rather than local loops.

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Improving the robustness of spatial networks by link addition: more and dispersed links perform better

Cited by 10 publications

References 52 publications

Data-Driven Bearing Fault Diagnosis of Microgrid Network Power Device Based on a Stacked Denoising Autoencoder in Deep Learning and Clustering by Fast Search without Data Labels

Data-Driven Bearing Fault Diagnosis of Microgrid Network Power Device Based on a Stacked Denoising Autoencoder in Deep Learning and Clustering by Fast Search without Data Labels

Health indicator construction for roller bearing based on an unsupervised deep belief network with a novel sigmoid zero local minimum point model

Adding links on minimum degree and longest distance strategies for improving network robustness and efficiency

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