omparison of Nonlinearity Measures based on Time Series Analysis for Nonlinearity Detection

Hosseini, SeyedMehrdad; Johansen, Tor Arne; Fatehi, Alireza

doi:10.4173/mic.2011.4.1

Cited by 16 publications

(6 citation statements)

References 31 publications

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“…Correlation-based analysis and higher order spectral analysis are the two useful methods to measure the non-linearity of dynamical systems. 21 In this section, we extend these methods to measure the non-linearity of MIMO systems.…”

Section: Non-linearity Measurementioning

confidence: 99%

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Automatic model bank selection in multiple model identification of gas turbine dynamics

Hosseini

Fatehi

Sedigh

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part I:

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A multiple model structure of a prototype industrial gas turbine system is constructed under normal operation using a systematic method that incorporates non-linearity measure and H-gap metric tools with the multiple models technique. First, two new non-linearity indices for multiple input-multiple output systems are introduced and employed for decomposing the operating space of a gas turbine into some linear and non-linear modes. The non-linear modes may be further partitioned into some linear modes. The input and output data in each of the linear modes are used to construct an initial multiple model structure. In order to avoid the increase of the number of linear local models, the H-gap metric is extended to multiple input-multiple output systems and used to measure the similarity between linear local models and to merge the similar models. As a result, an algorithm is proposed for construction of multiple linear local models. The algorithm is employed for the identification of a single-shaft prototype industrial gas turbine.

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Section: Non-linearity Measurementioning

confidence: 99%

“…There are many indices to measure the nonlinearity degree of a system. [19][20][21] However, two new non-linearity indices were presented in order to decompose the operating regime. The initial model bank is then constructed based on the obtained linear modes.…”

Section: Introductionmentioning

confidence: 99%

Automatic model bank selection in multiple model identification of gas turbine dynamics

Hosseini

Fatehi

Sedigh

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…Such methods usually require the time-consuming measurement process of many FRFs while relying on an experienced operator to detect and characterise non-linear behaviour correctly. In addition, measured noise in FRFs frequently causes significant problems, leading to the inapplicability of some techniques [7].…”

Section: Introductionmentioning

confidence: 99%

A method for detection and characterisation of structural non-linearities using the Hilbert transform and neural networks

Ondra

Sever

Schwingshackl

2017

Mechanical Systems and Signal Processing

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a b s t r a c tThis paper presents a method for detection and characterisation of structural non-linearities from a single frequency response function using the Hilbert transform in the frequency domain and artificial neural networks. A frequency response function is described based on its Hilbert transform using several common and newly introduced scalar parameters, termed non-linearity indexes, to create training data of the artificial neural network. This network is subsequently used to detect the existence of non-linearity and classify its type. The theoretical background of the method is given and its usage is demonstrated on different numerical test cases created by single degree of freedom nonlinear systems and a lumped parameter multi degree of freedom system with a geometric non-linearity. The method is also applied to several experimentally measured frequency response functions obtained from a cantilever beam with a clearance non-linearity and an under-platform damper experimental rig with a complex friction contact interface. It is shown that the method is a fast and noise-robust means of detecting and characterising non-linear behaviour from a single frequency response function.

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“…With the deepening of study, the nonlinear system identification methods have made significant progress in recent years, such as the probabilistic framework based on the well‐known Bayesian theorem, the widely used homogeneity method, and the frequency response function (FRFs)‐based method . In general, the nonlinear system identification methods can be classified as time domain, frequency domain, and time–frequency domain methods.…”

Section: Introductionmentioning

confidence: 99%

Structural dynamic nonlinear model and parameter identification based on the stiffness and damping marginal curves

Wang

Ding

Ren

et al. 2020

Struct Control Health Monit

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Summary This paper presents a novel structural dynamic nonlinear model and parameter identification method based on the stiffness and damping marginal curves. The stiffness and damping marginal curves are first extracted from the structural dynamic responses. Then, nine nonlinear feature indices (NFIs) are defined based on the marginal curves to describe the characteristics of various nonlinear models. To reduce the dimension of NFIs and facilitate the subsequent calculation of the support vector machine (SVM), the principal component analysis (PCA) is implemented. Afterwards, the NFIs processed by PCA are employed as the training samples to SVM classifier, which is used to identify the structural nonlinear model. According to the identified nonlinear model corresponding to the type of nonlinearity, the parameters of the nonlinear model are further identified by the nonlinear least square method. The numerical results of a single degree of freedom (SDOF) system and a cantilever beam structure demonstrate that the proposed method can effectively identify both nonlinear model and corresponding parameters under various excitations even considering the noise effect. Subsequently, the proposed method is also verified by a benchmark system subjected to a semisinusoidal pulse load and a beam bridge structure subjected to an earthquake load. The proposed method is also applied to the experimental test of a bolted joint cantilever beam subjected to a random excitation in the laboratory. Both numerical and test results demonstrate that the proposed method can identify both nonlinear model and corresponding parameters within good accuracy and robustness.

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omparison of Nonlinearity Measures based on Time Series Analysis for Nonlinearity Detection

Cited by 16 publications

References 31 publications

Automatic model bank selection in multiple model identification of gas turbine dynamics

Automatic model bank selection in multiple model identification of gas turbine dynamics

A method for detection and characterisation of structural non-linearities using the Hilbert transform and neural networks

Structural dynamic nonlinear model and parameter identification based on the stiffness and damping marginal curves

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