Operational Modal Analysis consists of estimating the modal parameters of civil/structural systems using only the response of the systems, while the unknown inputs are considered as realizations of white noise process...
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Operational Modal Analysis consists of estimating the modal parameters of civil/structural systems using only the response of the systems, while the unknown inputs are considered as realizations of white noise processes. Sometimes this last hypothesis is not correct, that is, the unknown input does not have a flat spectrum. The consequence is that the peaks of the input’s spectrum are observed in the system response, and, since the input is unmeasured, it is not possible to separate system poles (the modal parameters) from the input poles. The method proposed in this work is based on recording the response of the structure under different (unknown) excitations, including both white noise and non-white noise excitations. Then, a joint analysis of the records is performed: the common poles will correspond to system poles and the specific poles will correspond to input poles.
Remaining useful life(RUL) estimation based on condition monitoring data is central to condition based maintenance(CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the fail...
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Remaining useful life(RUL) estimation based on condition monitoring data is central to condition based maintenance(CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold(RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization(em) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.
Minimizing the energy consumption of wireless sensors is critical, yet a challenge for the design of wireless sensor networks (WSN). Energy is consumed in WSNs by sensing, communicating and processing. In various WSN ...
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Minimizing the energy consumption of wireless sensors is critical, yet a challenge for the design of wireless sensor networks (WSN). Energy is consumed in WSNs by sensing, communicating and processing. In various WSN applications, it is likely that communications are the major source of power consumption, rather than computation. Therefore, assuming that local processing is much less expensive than communicating, we focus on minimizing the number of transmissions for a distributed clustering problem in a sensor network. In our setup, each node in the network senses an environment that can be described as a mixture of Gaussians and each Gaussian component corresponds to one of the elementary conditions. For estimating the Gaussian components, we propose a distributed em algorithm based on stochastic approximation (Dem-SA), and we study a trade-off between local processing and communication for the distributed clustering problem. Dem-SA reduces the traffic and contention in a WSN by keeping computations and communications local and avoiding the need for cycles through the network. Simulation results will be presented. (C) 2016 Published by Elsevier B.V.
Recently, Mahmoudi and Mahmoodian [7] introduced a new class of distributions which contains univariate normal- geometric distribution as a special case. This class of distributions are very flexible and can be used q...
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Recently, Mahmoudi and Mahmoodian [7] introduced a new class of distributions which contains univariate normal- geometric distribution as a special case. This class of distributions are very flexible and can be used quite effectively to analyze skewed data. In this paper we propose a new bivariate distribution with the normalgeometric distribution marginals. Different properties of this new bivariate distribution have been studied. This distribution has five unknown parameters. The em algorithm is used to determine the maximum likelihood estimates of the parameters. We analyze one series of real data set for illustrative purposes.
This paper focuses on the estimation of power law type reliability trend analysis model given incomplete failure data from multiple homogeneous machines. In many real situations, we encounter data having missing parts...
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This paper focuses on the estimation of power law type reliability trend analysis model given incomplete failure data from multiple homogeneous machines. In many real situations, we encounter data having missing parts especially in the initial recording stage, so-called left censored data. We provide a method to estimate the failure intensity function of power law process using left censored data from multiple machines. The method consists of two folds: initially estimate parameters via the law of large numbers theory and revise the estimated parameters recursively through the em algorithm. The validity of our method is confirmed by simulation experiments. We also apply our method to the real-world case, Korean ARMY tank maintenance data, and show that our proposed method is applicable to practical maintenance planning.
Mixtures of factor analyzers (MFAs) have been popularly used to cluster the high-dimensional data. However, the traditional estimation method is based on the normality assumptions of random terms and thus is sensitive...
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Mixtures of factor analyzers (MFAs) have been popularly used to cluster the high-dimensional data. However, the traditional estimation method is based on the normality assumptions of random terms and thus is sensitive to outliers. In this article, we introduce a robust estimation procedure of MFAs using the trimmed likelihood estimator. We use a simulation study and a real data application to demonstrate the robustness of the trimmed estimation procedure and compare it with the traditional normality-based maximum likelihood estimate.
Inverse Weibull distribution has been used quite successfully to analyze lifetime data which has non-monotone hazard function. The main aim of this paper is to introduce bivariate inverse Weibull distribution along th...
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Inverse Weibull distribution has been used quite successfully to analyze lifetime data which has non-monotone hazard function. The main aim of this paper is to introduce bivariate inverse Weibull distribution along the same line as the Marshall Olkin bivariate exponential distribution, so that the marginals have inverse Weibull distributions. The proposed bivariate inverse Weibull distribution has four parameters and it has a singular component. Therefore, it can be used quite successfully if there are ties in the data. The joint probability density function, the joint cumulative distribution function and the joint survival function are all in closed forms. Several properties of this distribution have been discussed. It is observed that the proposed distribution can be obtained from the Marshall Olkin copula. The maximum likelihood estimators of the unknown parameters cannot be obtained in closed form, and we propose to use em algorithm to compute the maximum likelihood estimators. We propose to use parametric bootstrap method for construction of confidence intervals of the different parameters. We present some simulation experiments results to show the performances of the em algorithm and they are quite satisfactory. We provide the Bayesian analysis of the unknown parameters based on very flexible priors. We analyze one bivariate American Football League data set for illustrative purposes, and it is observed that this model provides a slightly better fit than some of the existing models. Finally, we present some generalization to the multivariate case.
We develop and apply an approach for analyzing multi-curve data where each curve is driven by a latent state process. The state at any particular point determines a smooth function, forcing the individual curve to swi...
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We develop and apply an approach for analyzing multi-curve data where each curve is driven by a latent state process. The state at any particular point determines a smooth function, forcing the individual curve to switch from one function to another. Thus each curve follows what we call a switching nonparametric regression model. We develop an em algorithm to estimate the model parameters. We also obtain standard errors for the parameter estimates of the state process. We consider three types of hidden states: those that are independent and identically distributed, those that follow a Markov structure, and those that are independent but with distribution depending on some covariate(s). A simulation study shows the frequentist properties of our estimates. We apply our methods to a building's power usage data. The Canadian Journal of Statistics 45: 442-460;2017 (c) 2017 Statistical Society of Canada
Linkage analysis has played an important role in understanding genome structure and evolution. However, two-point linkage analysis widely used for genetic map construction can rarely chart a detailed picture of genome...
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Linkage analysis has played an important role in understanding genome structure and evolution. However, two-point linkage analysis widely used for genetic map construction can rarely chart a detailed picture of genome organization because it fails to identify the dependence of crossovers distributed along the length of a chromosome, a phenomenon known as crossover interference. Multi-point analysis, proven to be more advantageous in gene ordering and genetic distance estimation for dominant markers than two-point analysis, is equipped with a capacity to discern and quantify crossover interference. Here, we review a statistical model for four-point analysis, which, beyond three-point analysis, can characterize crossover interference that takes place not only between two adjacent chromosomal intervals, but also over multiple successive intervals. This procedure provides an analytical tool to elucidate the detailed landscape of crossover interference over the genome and further infer the evolution of genome structure and organization.
This article proposes a mixture double autoregressive model by introducing the flexibility of mixture models to the double autoregressive model, a novel conditional heteroscedastic model recently proposed in the liter...
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This article proposes a mixture double autoregressive model by introducing the flexibility of mixture models to the double autoregressive model, a novel conditional heteroscedastic model recently proposed in the literature. To make it more flexible, the mixing proportions are further assumed to be time varying, and probabilistic properties including strict stationarity and higher order moments are derived. Inference tools including the maximum likelihood estimation, an expectation-maximization (em) algorithm for searching the estimator and an information criterion for model selection are carefully studied for the logistic mixture double autoregressive model, which has two components and is encountered more frequently in practice. Monte Carlo experiments give further support to the new models, and the analysis of an empirical example is also reported.
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