An Information Fusion Fault Diagnosis Method Based on Dimensionless Indicators With Static Discounting Factor and KNN

Xiong, Jiang; Zhang, Qinghua; Sun, Guifang; Zhu, Xiaojin; Лю, Мэй; Li, Zhiliang

doi:10.1109/jsen.2015.2497545

Cited by 94 publications

(35 citation statements)

References 10 publications

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“…In other words, the scope of the dimensionless indexes of normal equipment and fault equipment is difficult to distinguish, which makes the decision more difficult. To solve these problems, Xiong et al [22] proposed a genetic programming method based on dimensionless indexes in the time domain, which has achieved positive results in rotating machinery classification. However, constructing new dimensionless indexes with this method presents many deficiencies.…”

Section: Introductionmentioning

confidence: 99%

Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi‐Information Fusion

Xiao

Zhang

et al. 2019

Shock and Vibration

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Due to the complexity of the structure and process of large-scale petrochemical equipment, different fault characteristics are mixed and present multiple couplings and ambiguities, leading to the difficulty in identifying composite faults in rotating machinery. This paper proposes a composite faults diagnosis method for rotating machinery of the large unit based on evidence theory and multi-information fusion. The evidence theory and multi-information fusion method mainly deal with multisource information and conflict information, synthesize multiple uncertain information, and obtain synthetic information from multiple data sources. To detect faults in rotating machinery, the dimensionless index ranges of composite faults are first used to form a feature set as the reference. Then, a two-sample distribution test is applied to compare the known fault samples with the tested fault samples, and the maximum statistical distance is used. Finally, the multiple maximum statistical distances are fused by evidence theory and identifying fault types based on the fusion result. The proposed method was applied to the large petrochemical unit simulation experiment system, the results of which showed that our proposed method could accurately identify composite faults and provide maintenance guidance for composite fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi‐Information Fusion

Xiao

Zhang

et al. 2019

Shock and Vibration

Self Cite

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“…On the basis of the principal component factors described above, different classifiers are used to identify the PD experimental data. The classifiers used, in this study, were linear discriminant analysis (LDA) [30], k-nearest neighbor (KNN) [31], and support vector machine (SVM) algorithm [32]. LDA is a dimensionality reduction technology for supervised learning, which projects the sample onto a sorting line determining the category of the new sample based on the position of the projected point.…”

Section: Pattern Recognition Results Of Different Pd Patternsmentioning

confidence: 99%

A Novel Partial Discharge Detection Method Based on the Photoelectric Fusion Pattern in GIL

Zang

Qian

Liu³

et al. 2019

Energies

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Optical detection and ultrahigh frequency (UHF) detection are two significant methods of partial discharge (PD) detection in the gas-insulated transmission lines (GIL), however, there is a phenomenon of signals loss when using two types of detections to monitor PD signals of different defects, such as needle defect and free particle defect. This makes the optical and UHF signals not correspond strictly to the actual PD signals, and therefore the characteristic information of optical PD patterns and UHF PD patterns is incomplete which reduces the accuracy of the pattern recognition. Therefore, an image fusion algorithm based on improved non-subsampled contourlet transform (NSCT) is proposed in this study. The optical pattern is fused with the UHF pattern to achieve the complementarity of the two detection methods, avoiding the PD signals loss of different defects. By constructing the experimental platform of optical-UHF integrated detection for GIL, phase-resolved partial discharge (PRPD) patterns of three defects were obtained. After that, the image fusion algorithm based on the local entropy and the phase congruency was used to produce the photoelectric fusion PD pattern. Before the pattern recognition, 28 characteristic parameters are extracted from the photoelectric fusion pattern, and then the dimension of the feature space is reduced to eight by the principal component analysis. Finally, three kinds of classifiers, including the linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN), are used for the pattern recognition. The results show that the recognition rate of all the photoelectric fusion pattern under different classifiers is higher than that of optical and UHF patterns, up to the maximum of 95%. Moreover, the photoelectric fusion pattern not only greatly improves the recognition rate of the needle defect and the free particle defect, but the recognition accuracy of the floating defect is also slightly improved.

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“…Chen [13], G. H. B. Foo [14], M. Sepasi [15], T. Jiang [2], S. Zhai [16], M.Z [3], H. M [17] Data-drive I. Chen [5], J. Yan [6], M. Grbovic [7], S. Yin [8] In conclusion, data-driven and value-driven methods are widely employed in fault analysis, which makes fast and accurate analysis possible when faults occur. In recent years, scholars mainly focus on the following aspects: (1) value-driven, which mainly includes deep learning [22], integration, and fusion [23,24]. Zhang et al [25] and Wang et al [26] used deep learning in extraction and classification of fault feature.…”

Section: Methods Representativementioning

confidence: 99%

Fault Mechanism Analysis for Manufacturing System Based on Catastrophe Model

Zhu

Chen

Sun

2019

Mathematical Problems in Engineering

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Fault analysis is important in both research and industry. Current fault analysis tasks are mainly concerned with fault prediction and classification and do not focus enough on fault evolution mechanisms. In this paper, we propose a fault analysis method based on catastrophe theory for manufacturing system to improve the effectiveness and efficiency of real time monitoring of potential fault and causes analysis. The key advantages of our proposed method are (i) utilizing catastrophe theory and big data analysis to establish the fault cusp catastrophe model of manufacturing system and create the internal fault evolution mechanism of manufacturing system by the cusp catastrophe model and, (ii) with the established catastrophe model, fulfilling fault monitoring and accurate preventive control of the manufacturing system and ensuring the healthy operation of the manufacturing system.

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An Information Fusion Fault Diagnosis Method Based on Dimensionless Indicators With Static Discounting Factor and KNN

Cited by 94 publications

References 10 publications

Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi‐Information Fusion

Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi‐Information Fusion

A Novel Partial Discharge Detection Method Based on the Photoelectric Fusion Pattern in GIL

Fault Mechanism Analysis for Manufacturing System Based on Catastrophe Model

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