Information Fusion in Fault Diagnosis Based on B-Spline Networks

Benkhedda, H.; Patton, Ron J.

doi:10.1016/s1474-6670(17)42477-3

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…(1996), Gomm (1996), Hessel et al . (1996), Patton and Chen (1996), Zhang and Morris (1996), Alessandri and Parisini (1998), Benkhedda and Patton (1998), Boudaoud and Masson (1998), Chen et al . (1998), Dimla et al .…”

Section: Neural Network In Online Fault Detectionmentioning

confidence: 99%

Online expert systems for fault diagnosis in technical processes

Angeli

2008

Expert Systems

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It is generally accepted that there has been an increasing interest in online fault detection and diagnosis techniques for technical processes during the last few years. These techniques come from the artificial intelligence field or are classical numerical methods in combination with artificial intelligence methods. This paper presents a survey of recent research work in online expert systems for fault detection and diagnosis in technical processes. In addition, a short reference to other recent artificial intelligence methods for online fault detection is included and the main advantages and limitations of each method are illustrated.

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Section: Neural Network In Online Fault Detectionmentioning

confidence: 99%

Online expert systems for fault diagnosis in technical processes

Angeli

2008

Expert Systems

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Section: Introductionmentioning

confidence: 99%

“…In recent years, numerous scholars have made a great many of studies on the fault diagnosis of bearings based on multiple signal analysis methods, 3,4 including the denoising algorithm, 5–7 signal decomposition, 8–10 information fusion, 11–13 optimization, 14–16 and so on. Mingfu et al 17 studied the structure and dynamic characteristics of some model of dual-rotor aeroengine and deduced fault characteristic frequency formula of inter-shaft bearing.…”

Section: Introductionmentioning

confidence: 99%

Research on compound faults identification of aeroengine inter-shaft bearing based on CCF–Complexity–VMD–SVD

Fang

Guo

et al. 2022

Structural Health Monitoring

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Inter-shaft bearing is an important component of dual-rotor aeroengine and it is difficult to extract fault characteristics and identify a fault type. Therefore, the paper has proposed a method (CCF–Complexity–VMD–SVD) combining information fusion, Complexity parameter of Hjorth parameters, variational mode decomposition (VMD), and singular value decomposition (SVD). Firstly, to fully embody fault characteristic information, the method has established the cross-correlation function (CCF) of homologous acceleration information from the casing of one of the aeroengines (acceleration signals collected from the same section at the same moment in orthogonal directions) to blend homologous information, reinforce fault characteristic components, and lessen the influence of noise. Secondly, considering the great influence of signal complexity on the extraction of fault characteristics and the Complexity parameter of Hjorth parameters can express the complexity of signals better, the Complexity parameter of Hjorth parameters is placed into the fault characteristic extraction of inter-shaft bearing. The Complexity parameter is applied to optimize the number of layers and central frequency of VMD. Thirdly, signals are denoised through SVD to further lessen the influence of noise on characteristic extraction. Finally, frequency spectrum (FS) of signal denoised is used to make characteristic extraction and fault identification of compound faults of inter-shaft bearing. The result of comparative analysis with other methods has further illustrated the proposed method CCF–Complexity–VMD–SVD that can actualize the effective characteristic extraction of compound faults and precise identification in the early stage of fault based on vibration signals from casing of aeroengine. The performance of engineering appliance of the proposed method has been further verified through fault analysis of disassembly for the aeroengine.

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Application of B-Spline Neuro-Fuzzy Networks to Identification, Fault Detection and Isolation

Uppal

Patton

2000

IFAC Proceedings Volumes

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Information Fusion in Fault Diagnosis Based on B-Spline Networks

Cited by 5 publications

References 7 publications

Online expert systems for fault diagnosis in technical processes

Online expert systems for fault diagnosis in technical processes

Research on compound faults identification of aeroengine inter-shaft bearing based on CCF–Complexity–VMD–SVD

Application of B-Spline Neuro-Fuzzy Networks to Identification, Fault Detection and Isolation

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