Application of wavelets and neural networks to diagnostic system development, 1, feature extraction

Chen, B.H.; Wang, X.Z.; Yang, Shuang–Hua; McGreavy, C.

doi:10.1016/s0098-1354(99)00258-6

Cited by 79 publications

(30 citation statements)

References 12 publications

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“…Additional signal processing tools such as wavelet transform and Hilbert-Huang transform (HHT) analyses are needed to discover the relations between frequency features and machine states in-depth other than only centroids and peaks. Wavelet transform is one of the classical and widely used time-frequency analysis tool for non-stationary signals in manufacturing process monitoring [8,13,59]. Similarly, HHT performs time-frequency-energy analysis based on empirical mode decomposition and Hilbert spectral analysis for non-stationary signals [21,22].…”

Section: The Study Of Two Normal Statesmentioning

confidence: 99%

In situ monitoring of FDM machine condition via acoustic emission

Hai-xi

Wang

2015

Int J Adv Manuf Technol

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Fused deposition modeling (FDM) is one of the most popular additive manufacturing technologies for fabricating prototypes with complex geometry and different materials. However, current commercial FDM machines have the limitations in process reliability and product quality. In order to overcome these limitations and increase the levels of machine intelligence and automation, machine conditions need to be monitored more closely as in closedloop control systems. In this study, a new method for in situ monitoring of FDM machine conditions is proposed, where acoustic emission (AE) technique is applied. The proposed method allows for the identification of both normal and abnormal states of the machine conditions. The time-domain features of AE hits are used as the indicators. Support vector machines with the radial basis function kernel are applied for state identification. Experimental results show that this new method can potentially serve as a nonintrusive diagnostic and prognostic tool for FDM machine maintenance and process control.

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Section: The Study Of Two Normal Statesmentioning

confidence: 99%

In situ monitoring of FDM machine condition via acoustic emission

Hai-xi

Wang

2015

Int J Adv Manuf Technol

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“…Chen et al [25] presented an integrated framework for FDD which combined wavelet analysis and neural networks. Their model, which used multiscale wavelet analysis to determine the singularities of transient signals, proved to be effective in dealing with the noisy transient signals.…”

Section: Related Workmentioning

confidence: 99%

Robust model-based fault diagnosis for air handling units

Mulumba¹,

Afshari²,

Yan³

et al. 2015

Energy and Buildings

117

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“…71,72 In particular, in Refs. 70 and 73, it has been shown that a feature extraction phase, based on a wavelet decomposition of the transient data, increases significantly the classification accuracy.…”

Section: Appendix B: Data Preprocessing For Feature Selectionmentioning

confidence: 99%

Application of a niched Pareto genetic algorithm for selecting features for nuclear transients classification

Baraldi

Pedroni

Zio

2009

Int. J. Intell. Syst.

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Feature selection for transient classification is the problem of choosing among several monitored parameters (i.e., the features) to be used for efficiently recognizing the developing transient patterns. It is a critical issue for the application of "on condition" diagnostic techniques in complex systems, such as the nuclear power plants, where hundreds of parameters are measured. Indeed, irrelevant and noisy features have been shown to unnecessarily increase the complexity of the classification problem and degrade the diagnostic performance. In this paper, the problem of selecting the features to be used for efficient transient classification is tackled by means of multiobjective genetic algorithms. The approach leads to the identification of a family of equivalently optimal subsets of features, in the Pareto sense. However, difficulties in the convergence of the standard Pareto-based multiobjective genetic algorithm search in large feature spaces may arise in terms of representativeness of the identified Pareto front whose elements may turn out to be unevenly distributed in the objective functions space, thus not providing a full picture of the potential Pareto-optimal solutions. To overcome this problem, a niched Pareto genetic algorithm is embraced in this work. The performance of the feature subsets examined during the search is evaluated in terms of two optimization objectives: the classification accuracy of a Fuzzy K-Nearest Neighbors classifier and the number of features in the subsets. During the genetic search, the algorithm applies a controlled "niching pressure" to spread out the population in the search space so that convergence is shared on different niches of the Pareto front, which is thus evenly covered. The method is tested on a diagnostic problem characterized by a very large number of process features available for the classification of simulated transients in the feedwater system of a boiling water reactor. The dynamics of the transient signals is captured by wavelet decomposition, which actually increases the complexity of the search for the optimal feature subsets by triplicating the number of features to be considered. C 2008 Wiley Periodicals, Inc.

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Application of wavelets and neural networks to diagnostic system development, 1, feature extraction

Cited by 79 publications

References 12 publications

In situ monitoring of FDM machine condition via acoustic emission

In situ monitoring of FDM machine condition via acoustic emission

Robust model-based fault diagnosis for air handling units

Application of a niched Pareto genetic algorithm for selecting features for nuclear transients classification

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