Extension of REBMIX algorithm to von Mises parametric family for modeling joint distribution of wind speed and direction

Ye, X.W.; Xi, P.S.; Nagode, Marko

doi:10.1016/j.engstruct.2018.08.035

Cited by 21 publications

(19 citation statements)

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“…In [16], there is a suggestion to use the same model-selection procedure as described in Section 3.1.1, to select the optimal GMM parameters. However, it is reported in [17,18] that the model-selection procedure usually makes sub-optimal parameter estimates. This happens because the estimated parameters of the GMM with the REBMIX algorithm can be further optimized to yield higher values of the likelihood function (Equation 3) and therefore provide a beneficial impact on the values of the commonly used IC, for example, BIC, which is defined in Equation (10).…”

Section: Rebmix Algorithmmentioning

confidence: 99%

“…On other hand, in contrast to the EM, the parameters are estimated quickly. This can be seen in either the REBMIX application to the von-Mises MM parameter estimates in [17] or in the Weibull-Normal MM parameter estimates [18]. Because of this property, we think that REBMIX is a good algorithm for the initialization of the EM algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The initialization uses the Rough-enhanced-Bayes mixture estimation (REBMIX) algorithm [16]. REBMIX is a heuristic for estimating the parameters of the MM that provide a balance between the goodness of the MM parameter estimates and the estimation time [17,18]. In most cases, the parameter estimates with REBMIX are not as good as the ones estimated with the EM algorithm, although the parameter estimates seem to not deviate as much from the parameters estimated with the EM.…”

Section: Introductionmentioning

confidence: 99%

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Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

2020

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A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

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Section: Rebmix Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

2020

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“…The rough-enhanced-Bayes mixture estimation (REBMIX) algorithm [27] and [28] can be used to estimate the parameters of a GMM. The algorithm is a numerical procedure that combines an empirical density estimation, mode-finding, clustering and maximum-likelihood estimation procedures for the estimation of such parameters.…”

Section: Rebmix Approachmentioning

confidence: 99%

“…Since the rebmix is merely an heuristic, the final estimated parameters of GMM can be degenerated, which is the main disadvantage. On the other hand, it provides rapid estimation compared to the EM algorithm [28]. Additionally, the EM algorithm used for the other three methods may be trapped in a local optima and requires careful initialization [37].…”

Section: Rebmix-based Classificationmentioning

confidence: 99%

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

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Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring. Highlights• Gaussian-mixture-model-based classification was applied to the bearing-fault classification. • To discriminate the faulty from non-faulty bearings only simple statistics from vibrational data was used. • Two different datasets, the Case Western Rice University dataset and Bearing vibration data collected under time-varying rotational speed conditions dataset are used. • The Gaussian-mixture-model-based classification method showed to be a competitive and efficient method.

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Joint Model of Wind Speed and Corresponding Direction Based on Wind Rose for Wind Energy Exploitation

Yang

Lin²,

Dong³

2022

J. Ocean Univ. China

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Extension of REBMIX algorithm to von Mises parametric family for modeling joint distribution of wind speed and direction

Cited by 21 publications

References 20 publications

Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Joint Model of Wind Speed and Corresponding Direction Based on Wind Rose for Wind Energy Exploitation

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