Fault diagnosis of satellite power system using variable precision fuzzy neighborhood rough set

Suo, Mingliang; Zhang, Monan; Ding, Zhiying; Zhu, Baolong; Li, Shunli

doi:10.23919/chicc.2017.8028510

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Although the rough-set theory has been widely used in feature selection [31,32], in multi-granularity spaces, the traditional rough set methods cannot use accurate partition relations to construct multi-class equivalence relationships with different combinations of disturbance features to evaluate the uncertainty of sample distributions effectively, which may lead to poor multi-class equivalence relationships partition performance and poor anti-noise performance. Therefore, a variable precision fuzzy neighbourhood rough set is proposed to improve the similarity relation measure and antinoise ability [33,34], where the fuzzy neighbourhood rough set is introduced to measure the uncertainty of sample distribution by using the fuzzy similarity relation and adjusting the fine degree of granularity space partition. In addition, the variable precision parameter is introduced to adjust the fine degree of granularity space partition artificially to improve the anti-noise ability of the fuzzy neighbourhood rough set.…”

Section: Feature Selection Based On a Variable Precision Fuzzy Neighb...mentioning

confidence: 99%

Classification method for multiple power quality disturbances via label distribution enhancement and multi‐granular feature optimization

Ruan

et al. 2023

IET Generation Trans & Dist

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Large‐scale integration of distributed generation and widespread use of power electronic equipment make power quality disturbances (PQDs) more complicated. There are still unsolved problems for the classification of multiple power quality disturbances (MPQDs) that consist of various kinds of single disturbances: (1) since the difference between the contribution degrees of every contained single disturbance to the composite disturbance is ignored, the logical labels cannot completely describe the composite disturbance; (2) the optimal feature in one granular space may not be optimal in another. These drawbacks lead to the degradation of MPQD classification, which should be considered a multi‐label model rather than single‐label model used in existing methods. Therefore, this paper proposes a novel method to improve the classification performance of MPQDs. The label distribution, representing the contribution degree of a single disturbance to the composite disturbance, is introduced. In addition, the high‐dimensional feature space is reduced by multigranular optimization, where the fuzziness and redundancy are removed by the modified rough‐set method. To improve the performance of the classifier, the ensemble classification model based on homogeneous classifier integration is proposed to integrate the base classifiers constructed by the feature vectors from different granularity spaces. A large number of field recordings are applied to validate the proposed method. The results show that the proposed method performs better than traditional methods, especially under noisy environments.

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Section: Feature Selection Based On a Variable Precision Fuzzy Neighb...mentioning

confidence: 99%

Classification method for multiple power quality disturbances via label distribution enhancement and multi‐granular feature optimization

Ruan

et al. 2023

IET Generation Trans & Dist

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“…. , X N , B ⊆ A generates the neighborhood relation R on U , then the lower approximation and the upper approximation of the neighborhood of the decision D for B can be expressed by the formula (11): The decision boundary can be expressed as:…”

Section: B Fuzzy Hybrid Incomplete Neighborhood Rough Modelmentioning

confidence: 99%

“…In the experiment, the algorithm proposed in this paper (IDFS-VTRS), the Dynamic variable precision rough set model of mixed information system (MIS-DPRS) proposed in the literature [29], the feature selection method adopted the binary particle swarm optimization based on the mutation operator [30], best first forward search (BFFS) and best first backward search (BFBS) algorithm provided by Weka were used to select the characteristics of the test data. The classification and evaluation indexes of each algorithm are shown in table 11. Table 11 shows that the evaluation indexes of Accuracy, Recall and Precision of the IFDS-VTRS algorithm are the best among the four-test data.…”

Section: ) Comparative Experiments On Evaluation Indexes Of Differenmentioning

confidence: 99%

“…Aiming at the problem of low diagnosis accuracy and not easy to obtain incomplete data, the neighborhood rough set and signed directed graph are combined to improve the diagnosis efficiency without prior knowledge. To reduce the impact of noisy data, Suo et al [11] proposed a diagnosis approach based on a variable precision fuzzy neighborhood rough set (VPFNRS) model, which could extract fuzzy rules from hybrid data with noises and make fuzzy diagnosis results based on the extracted fuzzy rule model and the weights of condition attributes. Wang et al [12] designed a variableprecision fuzzy neighborhood rough set model to reduce the possibility of samples being misclassified.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Feature Selection Approach Based on IFDS and DPSO With Variable Thresholds in Complex Data Environments

Kejia

et al. 2020

IEEE Access

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Neighborhood rough model is widely used in feature selection with high dimension, fuzzy, continuous and discrete attributes, incomplete data and so on, and the application of neighborhood rough model depends on neighborhood threshold. In the application of the model, the point-value neighborhood threshold is not adaptive, which leads to low classification accuracy and high time complexity of the algorithm. In order to solve the above problems, a feature selection approach based on IFDS (Incomplete Fuzzy Hybrid Decision System) and DPSO (Discrete Particle Swarm Optimization Algorithm) with variable thresholds is proposed. Firstly, a neighborhood rough model capable of simultaneously processing fuzzy, hybrid and incomplete data was established. The average reachable distance was introduced to construct the attribute neighborhood threshold set and reduce the interference of noise data on classification accuracy. Secondly, we constructed the DPSO particle fitness function using the feature subset length, the significance of the attribute and the negative domain of the neighborhood, and improved the inertia weight computing method, so as to enhance the feature selection speed and the feature subset quality. Finally, the simulation experiment was performed using the real industrial production data. The experiment effect shows that this method has obvious advantages in improving the classification accuracy, optimizing the search speed and the optimal feature subset quality. INDEX TERMS Feature selection, fuzzy, hybrid and incomplete data, incomplete fuzzy hybrid decision system (IFDS), adaptive neighborhood threshold, discrete particle swarm optimization algorithm (DPSO).

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A survey on granular computing and its uncertainty measure from the perspective of rough set theory

et al. 2019

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Fault diagnosis of satellite power system using variable precision fuzzy neighborhood rough set

Cited by 5 publications

References 14 publications

Classification method for multiple power quality disturbances via label distribution enhancement and multi‐granular feature optimization

Classification method for multiple power quality disturbances via label distribution enhancement and multi‐granular feature optimization

Research on the Feature Selection Approach Based on IFDS and DPSO With Variable Thresholds in Complex Data Environments

A survey on granular computing and its uncertainty measure from the perspective of rough set theory

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