A multi-team competitive optimization algorithm for bearing fault diagnosis

Zheng, Bo; Huang, Hong‐Zhong; Xu, Huifu; Meng, Debiao

doi:10.1109/rams.2016.7447990

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…Hence, numbers of scales for each feature module and levels of feature modules were manually selected by a trial‐and‐error method. Future work includes optimization of the RoMP Net structure and hyperparameters used in the proposed network by addressing novel optimization methods, including genetic algorithm, 23 gray wolf optimizer, 24 or multiteam competitive optimization algorithm 25 …”

Section: Methodsmentioning

confidence: 99%

“…Future work includes optimization of the RoMP Net structure and hyperparameters used in the proposed network by addressing novel optimization methods, including genetic algorithm, 23 gray wolf optimizer, 24 or multiteam competitive optimization algorithm. 25 Each feature extraction module was constructed as a format of an autoencoder that consists of encoding and decoding parts. Each encoding and decoding part consisted of six scaled layers.…”

Section: Construction Of Romp Netmentioning

confidence: 99%

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Rotational multipyramid network with bounding‐box transformation for object detection

Kim

Jeong

et al. 2021

Int J of Intelligent Sys

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The study proposes a rotational multipyramid network (RoMP Net) with bounding‐box transformation for object detection. The RoMP Net is a single‐stage object detection neural network featuring three characteristics. First, the network uses a rotational bounding box to minimize the effect of background images when extracting features of objects. Bounding‐box transformation was proposed to compensate for the limitation of the rotational bounding boxes, which have relatively low prediction accuracy for objects with a high aspect ratio. Second, the RoMP Net introduces a multi‐scale and multi‐level feature pyramid network to extract distinct and semantic features efficiently. This network architecture ensures high prediction accuracy and robustness regardless of the size and complexity of objects. Third, hyperparameters in the bounding boxes are automatically determined through an unsupervised clustering method. This optimization method is also critical in improving accuracy. The performance of the proposed network and preprocessing methods are validated through image‐sets comprising critical components in power transmission facilities, which have a variety of sizes and aspect ratios. This case study demonstrates the effectiveness and robustness of the three key characteristics in the RoMP Net. Furthermore, the RoMP Net outperforms other state‐of‐the‐art deep neural networks in prediction accuracy and robustness for object detection. Specifically, the mean average precision of the RoMP Net in the validation image‐sets shows that it has the highest prediction accuracy, whereas its values in the test image‐sets confirm the network's robustness. The fast yet accurate RoMP Net will expand the range of object detection through deep neural networks.

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Section: Methodsmentioning

confidence: 99%

Section: Construction Of Romp Netmentioning

confidence: 99%

Rotational multipyramid network with bounding‐box transformation for object detection

Kim

Jeong

et al. 2021

Int J of Intelligent Sys

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“…The advantage of proposed method is that it can give the distribution of similarity matrix intuitively, which is conducive to reduce the dependency on the experiences of experts, meanwhile, the granular generation can be realized. Furthermore, the data of bearing outer and inner race fault (Zheng et al, 2016) will be used to verify the merit of dimensionless similarity by comparisons with the methods cited from Refs. (Zhang, 2000) and (Huang et al, 2007), the two kinds of fault respectively contain 60 samples, and the attributes of sample have different dimensions.…”

Section: Graph Generationmentioning

confidence: 99%

“…For classification algorithms based on iteration operations, the number of iteration is always influenced by the number of training samples. Support vector machine (SVM) optimized by particle swarm optimization (PSO) (Zheng, 2013), the learning vector quantization (LVQ) network (Biswal et al, 2014), the back propagation (BP) network (Ali et al, 2015), and the kernel multi-team competitive optimization (k-MTCO) (Zheng, 2016) are used to verify the applicability of granular computing. Some more detailed information can be found in the cited references.…”

Section: Influence Of Gpds On Classification Algorithmsmentioning

confidence: 99%

Graph Partition Based on Dimensionless Similarity and Its Application to Fault Diagnosis

Zheng,

Huang,

Zhou

et al. 2017

PHMAP_CONF

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To improve the efficiency of fault diagnosis, a novel granular computing algorithm is developed to reduce computational cost. It is realized by extracting and partitioning on the complete graphs, and in the process of graph generation, the graph partition based on dimensionless similarity (GPDS) method is proposed to overcome the influence of attributes with different dimensions. The algorithm is named graph partition based on dimensionless similarity. Moreover, similarity threshold determination method based on frequency distribution histogram is proposed to reduce the dependency on the experiences of experts. Meanwhile, a weighted relative error is proposed to measure quantitatively the distribution change of original data after being compressed. Finally, different characteristic data are applied to verify the theories. The experimental results indicate that the compressed training samples can maintain the classification accuracy. Furthermore, the elapsed time can be obviously reduced. Therefore, the GPDS method can be used in fault diagnosis properly

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“…Traditional PSO can inherit many beneficial characteristics from human behavior, so that PSO variants have better performances in the engineering application. For example, these PSO variants may be based on human cognitive psychology [27], comprehensive learning strategy [28], human organization leadership behavior [29], aging mechanism of human society [30], human competitive and cooperative behavior [31], and so on.…”

Section: Introductionmentioning

confidence: 99%

Team Collaboration Particle Swarm Optimization and Its Application on Reliability Optimization

Zheng¹,

Ma²,

Zhang³

et al. 2021

IJCIS

Self Cite

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Particle swarm optimization (PSO) tends to be premature convergence due to easily trapping into local suboptimal areas. In order to overcome the PSO's defects, the reasons causing the defects are analyzed and summarized as population diversity deficiency, insufficient information sharing, unbalance of exploitation and exploration, and single update strategy. On this basis, inspired by human team collaboration behavior, a team collaboration particle swarm optimization (TCPSO) is proposed. Diversified updates strategies, dynamic grouping strategy, selectivity vector, and decreasing and increasing inertia weight are designed in TCPSO to solve the defects' reasons and improve the optimization performance. Eight typical test functions have been used to evaluate and compare the performance of different PSO variants, and the results have been proven that the optimal results found by TCPSO are better compared with other PSO variants, which demonstrates the rationality and effectiveness of TCPSO. Finally, a real-world problem for reliability optimization are solved by five algorithms, and the results prove the convergence rate and stable optimization performance of TCPSO, TCPSO can provide better support for reliability optimization of complex system.

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A multi-team competitive optimization algorithm for bearing fault diagnosis

Cited by 6 publications

References 5 publications

Rotational multipyramid network with bounding‐box transformation for object detection

Rotational multipyramid network with bounding‐box transformation for object detection

Graph Partition Based on Dimensionless Similarity and Its Application to Fault Diagnosis

Team Collaboration Particle Swarm Optimization and Its Application on Reliability Optimization

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