Research of practical expert system for transformer fault diagnosis based on multidimensional data fusion

Xu, Yang; Xie, Tianxi; Zhou, Zhicheng; Lu, Yuexiang; Jia, Jun; Chen, Shu; Li, Jiansheng; Ma, Yukun; Jiang, Chao

doi:10.1109/appeec.2016.7779516

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…In order to include complete working cycle of diesel engine. The length of segment can be expressed as (19).…”

Section: Experimental Analysis Based On Optimized Expectaion-maximmentioning

confidence: 99%

“…For these reasons, multi-sensor information fusion technology is gradually attached the weight of researchers. Multi-sensors information resources can be fully and effectively utilized through data fusion method to gain the greatest resources of system under test in different view angles [19]. However, the increase of features has a negative impact, such as information redundancy and complex data processing on classification, and even reduce the recognition rate [20].…”

Section: Introductionmentioning

confidence: 99%

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Engine Working State Recognition Based on Optimized Variational Mode Decomposition and Expectation Maximization Algorithm

Lin

et al. 2020

IEEE Access

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Increasingly energy and environmental crises put forward higher request on diesel engine. It promotes the development of diesel engine, while the complexity of structure is much higher, which leads to higher probability of faults. In order to recognize the states of engine in harsh environments effectively, variational mode decomposition (VMD) and expectation maximization (EM) are introduced into this paper to analyze multi-channel vibration signals. To select the decomposition level of VMD adaptively, a novel power spectrum segmentation based on scale-space representation is proposed for the optimization of VMD and results show this approach can discriminate different frequency components in high noise circumstance accurately and efficiently. To improve the adaptability and accuracy of EM, a feature selection approach based on genetic algorithm (GA) is introduced to preprocess original data and a cross validation method is used for selecting cluster number adaptively. Combined with these approaches, a diesel engine state recognition scheme based on multi-channel vibration signals using optimized VMD and EM is proposed. Compared with existing method, this scheme shows great advantages in accuracy and efficiency, and could be applied in actual engineering.

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“…In order to include complete working cycle of diesel engine. The length of segment can be expressed as (19).…”

Section: Experimental Analysis Based On Optimized Expectaion-maximmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Engine Working State Recognition Based on Optimized Variational Mode Decomposition and Expectation Maximization Algorithm

Lin

et al. 2020

IEEE Access

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“…It is shown in the literature [6] that an intelligent lighting system for an exhibition hall network, which may be a scene illuminance sensor installed according to the moment of sunlight illumination change, sets a different direct light brightness for exhibits when visitors approach, and also sets up the illumination according to the peak and trough time of the audience entering. Based on multi-dimensional data fusion, a power transformer fault diagnosis system showed the algorithm efficiency in the study [7]. The fuzzy clustering algorithm of the authors of [8] simulated the hydrological process without the relevant data, and better reflected the process of the runoff variation within the error range.…”

Section: Introductionmentioning

confidence: 99%

“…The above algorithms [1,2,3,4,5,6,7,8] were based on real-datasets and information fusion, and these algorithms assumed, directly or indirectly, that all observations are subject to normal distributions with the same parameter. All of the observations were located near a true value with the same weight.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Fusion Algorithm Based on Improved Evidence Theory and Clustering

Wang

Yuan

Zhang

et al. 2019

Sensors

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In most of the application scenarios of industrial control systems, the switching threshold of a device, such as a street light system, is typically set to a fixed value. To meet the requirements for a smart city, it is necessary to set a threshold that is adaptive to different conditions by fusing the multi-attribute observations of the sensors. This paper proposes a multi-attribute fusion algorithm based on fuzzy clustering and improved evidence theory. All of the observations are clustered by fuzzy clustering, where a proper clustering method is chosen, and the improved evidence theory is used to fuse the observations. In the experiments, two-dimensional observations for the street light illumination and for the ambient illumination are used in a campus-intelligent lighting system based on a narrowband Internet of things, and the results demonstrate the effectiveness of the proposed fusion algorithm. The proposed algorithm can be applied to a variety of multi-attribute fusion scenarios.

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“…During the process of fault diagnosis, multi‐sensor data and the information from other sources are fused with the technology of information fusion to make correct judgments and decisions by cognizing and describing the objects more accurately and fully . Nowadays, the usual ways of information fusion are classical reasoning, Kalman filter method, Bayes estimation method, and cluster analysis, and so on .…”

Section: Introductionmentioning

confidence: 99%

Synthesized fault diagnosis method reasoned from rough set‐neural network and evidence theory

Yang

2018

Concurrency and Computation

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Summary When traditional machinery fault diagnosis methods are used to handle diagnostic problems, the problems such as low diagnosis accuracy and bad real‐time capability will arise if there are lots of data and various complex faults. An integrated fault diagnosis reasoning strategy based on fusing rough sets, neural network, and evidence theory is presented using the principles of data fusion and meta‐synthesis. Firstly, use the the parallel neural network structure to improve diagnosis ability of the local diagnosis networks; preprocess the data with rough set theory to simplify the complex neural networks; and eliminate redundant properties in order to determine the topological structure of network. By this way, the shortcomings of network, such as large scale and slow classification, can be overcome. Secondly, a new objectified method of basic probability assignment is given. Besides, the accuracy and efficiency of the fault diagnosis can be improved obviously according to the various redundant and complementary fault information by using the combination rule of the evidence theory to synthesize and make decisions on the evidence. The example of rotating machinery diagnostic given in the paper proves the method to be feasible and available.

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Research of practical expert system for transformer fault diagnosis based on multidimensional data fusion

Cited by 6 publications

References 4 publications

Engine Working State Recognition Based on Optimized Variational Mode Decomposition and Expectation Maximization Algorithm

Engine Working State Recognition Based on Optimized Variational Mode Decomposition and Expectation Maximization Algorithm

Multi-Attribute Fusion Algorithm Based on Improved Evidence Theory and Clustering

Synthesized fault diagnosis method reasoned from rough set‐neural network and evidence theory

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