Detection and classification of non-linearities based on Volterra kernels processing

Aiordăchioaie, Dorel; Ceangă, Emil; Keyser, Robin De; Naka, Yuji

doi:10.1016/s0952-1976(01)00024-0

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…. , ( − + 1)] and the output is ( ) = ⇀ ( + 1), the system function can construct nonlinear prediction model with the expansion of Volterra series as given by [31] ⇀ (…”

Section: Volterra Seriesmentioning

confidence: 99%

“…To address these issues, this paper presents a new signal processing method, named Volterra series assisted undecimated lifting wavelet packet transform, by extending our prior work [26]. First of all, Volterra series [27], as a boundary treatment method, is used to extend both ends of the signal. Then, the wavelet coefficients, decomposed by the undecimated lifting wavelet packet, are chopped back to original length to serve as the signals of interest for machinery incipient fault detection.…”

Section: Introductionmentioning

confidence: 99%

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Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

Duan

Wang

et al. 2016

Shock and Vibration

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Effective signal processing in fault detection and diagnosis (FDD) is an important measure to prevent failure and accidents of machinery. To address the end distortion and frequency aliasing issues in conventional lifting wavelet transform, a Volterra series assisted undecimated lifting wavelet packet transform (ULWPT) is investigated for machinery incipient fault diagnosis. Undecimated lifting wavelet packet transform is firstly formulated to eliminate the frequency aliasing issue in traditional lifting wavelet packet transform. Next, Volterra series, as a boundary treatment method, is used to preprocess the signal to suppress the end distortion in undecimated lifting wavelet packet transform. Finally, the decomposed wavelet coefficients are trimmed to the original length as the signal of interest for machinery incipient fault detection. Experimental study on a reciprocating compressor is performed to demonstrate the effectiveness of the presented method. The results show that the presented method outperforms the conventional approach by dramatically enhancing the weak defect feature extraction for reciprocating compressor valve fault diagnosis.

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“…. , ( − + 1)] and the output is ( ) = ⇀ ( + 1), the system function can construct nonlinear prediction model with the expansion of Volterra series as given by [31] ⇀ (…”

Section: Volterra Seriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

Duan

Wang

et al. 2016

Shock and Vibration

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“…Volterra kernels have been widely employed in identification of nonlinear systems in understanding the nonlinearity of the systems [1,4,16,18,20,28,29,35,41,48]. Specifically, the Volterra model of order two was used in [18] for control design in the chemical/petrochemical industries, and in [28] for predictive control of a simulated multivariable polymerization reactor.…”

Section: Introductionmentioning

confidence: 99%

“…Volterra models were also frequently used in artificial intelligence and signal processing. For instance, a nonlinear system in pattern recognition was modeled by a Volterra model [1]. Since many signals of interest are generated by nonlinear sources or are processed by nonlinear systems, Volterra modeling was introduced in signal processing for estimating and controlling an active noise arising from dynamical systems [41].…”

Section: Introductionmentioning

confidence: 99%

Multiparameter regularization for Volterra kernel identification via multiscale collocation methods

Brenner

Jiang

2008

Adv Comput Math

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Identification of the Volterra system is an ill-posed problem. We propose a regularization method for solving this ill-posed problem via a multiscale collocation method with multiple regularization parameters corresponding to the multiple scales. Many highly nonlinear problems such as flight data analysis demand identifying the system of a high order. This task requires huge computational costs due to processing a dense matrix of a large order. To overcome this difficulty a compression strategy is introduced to approximate the full matrix resulted in collocation of the Volterra kernel by an appropriate sparse matrix. A numerical quadrature strategy is designed to efficiently compute the entries of the compressed matrix. Finally, numerical results of three simulation experiments are presented to demonstrate the accuracy and efficiency of the proposed method. Communicated by Juan Manuel Peña. 422 M. Brenner et al.

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“…Thus, and in conjunction with an artificial neural-network scheme, the detection and classification of any inherent nonlinearities that may exist and lead to any kind of fault or structural failure is presented by Aiordachioaie et al (2001).…”

Section: An Overview Of Volterra System Identification Schemes and Techniquesmentioning

confidence: 99%

Nonlinear system modeling and identification of an onshore oscillating water column - wave energy converter, using Volterra series and Hilbert-Huang transform

Γκίκας¹

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Ο κύριος στόχος της Δ.Δ. είναι να αναπτυχθεί ένα συστημικό μοντέλοκατάλληλο να περιγράψει την λειτουργία της συσκευής κυματικήςενέργειας με ταλαντευόμενη στήλη ύδατος, επικεντρώνοντας τοενδιαφέρον στην μεταβολή της δυναμικής πίεσης μέσα στο κλωβό τηςσυσκευής.Η μεθοδολογία που αναπτύχθηκε περιλαμβάνει την χρησιμοποίησησειρών Volterra και μετασχηματισμού Hilbert-Huang με βάση τα οποίακατασκευάστηκε ακριβές και εύρωστο συστημικό μοντέλο για το μη-γραμμικο μέρος του συστήματος.Τα αποτελέσματα κρίνονται ως άκρως ικανοποιητικά και η μεθοδολογίαπου αναπτύχθηκε εκτός του υπολογιστικού κόστους που εξοικονομείμπορεί να εφαρμοστεί ακόμα σε μια πλειάδα μη-γραμμικών δυναμικώνσυστημάτων.

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Detection and classification of non-linearities based on Volterra kernels processing

Cited by 4 publications

References 6 publications

Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

Multiparameter regularization for Volterra kernel identification via multiscale collocation methods

Nonlinear system modeling and identification of an onshore oscillating water column - wave energy converter, using Volterra series and Hilbert-Huang transform

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