A black box identification in frequency domain

Mercorelli, Paolo; Terwiesch, Peter

doi:10.1002/etep.4450130105

Cited by 10 publications

(9 citation statements)

References 12 publications

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“…Let R c = ψ c d,j,n (t); (d, n) ∈ Z, j ∈ N, t ∈ and R s = ψ s d,j,n (t); (d, n) ∈ Z, j ∈ N, t ∈ be the truncated cosine and sine packet frames respectively as defined in [7]. The algorithm is developed as follows.…”

Section: Denoising and Harmonic Detection Algorithmmentioning

confidence: 99%

“…In on-line detection of harmonic features interesting contributions have been presented in [5][6] where solutions to the problem of detection of dominant frequency vibration in pantograph systems are proposed. It has been highlighted in [7][8], that one of the most important problems in rail vehicle control is to model the nonlinearity of the locomotive transformer as well as to classify the transformer inrush current. Progress in this direction is marked by [7] which proposes an efficient algorithm in order to model strong non-linear systems.…”

Section: Motivationsmentioning

confidence: 99%

“…It has been highlighted in [7][8], that one of the most important problems in rail vehicle control is to model the nonlinearity of the locomotive transformer as well as to classify the transformer inrush current. Progress in this direction is marked by [7] which proposes an efficient algorithm in order to model strong non-linear systems. The transformer inrush current is caused by the transformer nonlinearity and occurs with the discontinuity of the magnetic flow.…”

Section: Motivationsmentioning

confidence: 99%

“…2 seems to suggest a function with a frequency window and time support. As shown in [7] a suitable family for this case is the smooth trigonometric wavelet packet. By choosing adjacent functions in particular, orthonormal bases are obtained.…”

Section: Choosing the Best Regressor Family: Smooth Trigonometric Wavmentioning

confidence: 99%

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Denoising and Harmonic Detection Using Nonorthogonal Wavelet Packets in Industrial Applications

Mercorelli

2007

Jrl Syst Sci & Complex

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New industrial applications call for new methods and new ideas in signal analysis. Wavelet packets are new tools in industrial applications and they have just recently appeared in projects and patents. In training neural networks, for the sake of dimensionality and of ratio of time, compact information is needed. This paper deals with simultaneous noise suppression and signal compression of quasi-harmonic signals. A quasi-harmonic signal is a signal with one dominant harmonic and some more sub harmonics in superposition. Such signals often occur in rail vehicle systems, in which noisy signals are present. Typically, they are signals which come from rail overhead power lines and are generated by intermodulation phenomena and radio interferences. An important task is to monitor and recognize them. This paper proposes an algorithm to differentiate discrete signals from their noisy observations using a library of nonorthonormal bases. The algorithm combines the shrinkage technique and techniques in regression analysis using Shannon Entropy function and Cross Entropy function to select the best discernable bases. Cosine and sine wavelet bases in wavelet packets are used. The algorithm is totally general and can be used in many industrial applications. The effectiveness of the proposed method consists of using as few as possible samples of the measured signal and in the meantime highlighting the difference between the noise and the desired signal. The problem is a difficult one, but well posed. In fact, compression reduces the level of the measured noise and undesired signals but introduces the well known compression noise. The goal is to extract a coherent signal from the measured signal which will be "well represented" by suitable waveforms and a noisy signal or incoherent signal which cannot be "compressed well" by the waveforms. Recursive residual iterations with cosine and sine bases allow the extraction of elements of the required signal and the noise. The algorithm that has been developed is utilized as a filter to extract features for training neural networks. It is currently integrated in the inferential modelling platform of the unit for Advanced Control and Simulation Solutions within ABB's industry division. An application using real measured data from an electrical railway line is presented to illustrate and analyze the effectiveness of the proposed method. Another industrial application in fault detection, in which coherent and incoherent signals are univocally visible, is also shown.

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Section: Denoising and Harmonic Detection Algorithmmentioning

confidence: 99%

Section: Motivationsmentioning

confidence: 99%

Section: Motivationsmentioning

confidence: 99%

Section: Choosing the Best Regressor Family: Smooth Trigonometric Wavmentioning

confidence: 99%

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Denoising and Harmonic Detection Using Nonorthogonal Wavelet Packets in Industrial Applications

Mercorelli

2007

Jrl Syst Sci & Complex

Self Cite

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show abstract

“…In particular, when the problem consists of classifying signals into known categories, the approach is similar to a nonparametric estimation. Detailed works are for instance [1,2]. This paper proposes a method, in learning machines, for the detection and classification of time-frequency phenomena by using wavelet packets in wavelet networks with biorthogonal functions.…”

Section: Introduction and Problem Definitionmentioning

confidence: 99%

Biorthogonal wavelet trees in the classification of embedded signal classes for intelligent sensors using machine learning applications

Mercorelli

2007

Journal of the Franklin Institute

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A Theoretical Multiscale Analysis of Electrical Field for Fuel Cells Stack Structures

Cattani

Mercorelli

Villecco

et al. 2006

Computational Science and Its Applications - ICCSA 2006

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A black box identification in frequency domain

Abstract: Harmonic oscillations as integer multiples of the fundamental frequency in a power

Cited by 10 publications

References 12 publications

Denoising and Harmonic Detection Using Nonorthogonal Wavelet Packets in Industrial Applications

Denoising and Harmonic Detection Using Nonorthogonal Wavelet Packets in Industrial Applications

Biorthogonal wavelet trees in the classification of embedded signal classes for intelligent sensors using machine learning applications

A Theoretical Multiscale Analysis of Electrical Field for Fuel Cells Stack Structures

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