Sharif Digital Repository

Tolerances significantly affect the assemblability of components, the product's performance, and manufacturing cost in mechanical assemblies. Despite the importance of product reliability assessment, the reliability-based tolerance design of mechanical assemblies has not been previously considered in the literature. In this paper, a novel method based on Bayesian modeling is proposed for the tolerance-reliability analysis and allocation of complex assemblies where the explicit assembly functions are difficult or impossible to extract. To reach this aim, a Bayesian model is developed for tolerance-reliability analysis. Then, a multi-objective optimization formulation is proposed for obtaining... 

Uncertainties can be divided into two general categories: aleatory and epistemic. Conventional reliability-based robust design optimization approaches, which disregard epistemic uncertainties due to lack of knowledge about the physical nature of systems, have previously been developed. To overcome this weakness, unlike previous methods, a Bayesian reliability-based robust design optimization method is proposed in the presence of both aleatory and epistemic uncertainties. The proposed formulation is presented as a multi-objective optimization problem. The univariate dimension reduction method is used to approximate the mean and variance of the design function. The non-dominated sorting... 

A TI-ADC is a circuitry to achieve high sampling rates by passing the signal and its shifted versions through a number of parallel ADCs with lower sampling rates. When the time shifts between the C channels of a TI-ADC are properly tuned, the aggregate of the obtained samples is equivalent to that of a single ADC with C-times the sampling rate. However, the performance of a TI-ADC can be seriously degraded under interchannel timing mismatch. As this non-ideality cannot be avoided in practice, we need to first estimate the mismatch value, and then, compensate it. In this paper, by adopting a stochastic bandlimited signal model we study the signal recovery problem from the samples of a TI-ADC... 

Purpose: The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly functions are difficult or impossible to extract based on Bayesian modeling. Design/methodology/approach: In the proposed method, first, tolerances are modelled as the random uncertain variables. Then, based on the assembly data, the explicit assembly function can be expressed by the Bayesian model in terms of manufacturing and assembly tolerances. According to the obtained assembly tolerance, reliability of the mechanical assembly to meet the assembly requirement can be estimated by a proper first-order... 

Herein, we propose a method based on the existing second-order blind identification of underdetermined mixtures technique for identifying the modal characteristics - namely, natural frequencies, damping ratio, and real-valued partial mode shapes of all contributing modes - of structures with a limited number of sensors from recorded free/ambient vibration data. In the second-order blind identification approach, second-order statistics of recorded signals are used to recover modal coordinates and mode shapes. Conventional versions of this approach require the number of sensors to be equal to or greater than the number of active modes. In the present study, we first employ a parallel factor... 

In this paper we propose a novel hierarchical Bayesian model for sparse regression problem to use in semi-supervised hyperspectral unmixing which assumes the signal recorded in each hyperspectral pixel is a linear combination of members of the spectral library contaminated by an additive Gaussian noise. To effectively utilizing the spatial correlation between neighboring pixels during the unmixing process, we exploit a Markov random field to simultaneously group pixels to clusters which are associated to regions with homogeneous mixtures in a natural scene. We assume Sparse fractional abundances of members of a cluster to be generated from an exponential distribution with the same rate... 

A downscaling model capable of explaining the temporal and spatial variability of regional hydroclimatic variables is essential for reliable climate change studies and impact assessments. This study proposes a novel statistical approach based on generalized hierarchical linear model (GHLM) to downscale precipitation from the outputs of general circulation models (GCMs) at multiple sites. GHLM partitions the total variance of precipitation into within- and between-site variability allowing for transferring information between sites to develop a regional downscaling model. The methodology is demonstrated by downscaling precipitation using the outputs of eight GCMs in Lake Urmia basin in... 

Marine structures are typically sensitive to the direction of wind and waves, especially in extreme metocean conditions. The extreme metocean conditions and their associated predicted directions are not easily reachable from traditional design methodologies. In this research, the most probable combinations of different extreme metocean conditions along with their associated direction are predicted for the HyWind Scotland wind farm, Scotland. To achieve this, the Hierarchical Bayesian Modeling approach is applied to define the Joint Probability Distribution Function (JPDF) of four combinations of metocean parameters, including wave direction, wind direction and wind-wave misalignment. The...