Estimating process capability indices of multivariate nonnormal processes

The capability analysis of production processes where there are more than one correlated quality variables is a complicated task. The problem becomes even more difficult when these variables exhibit nonnormal characteristics. In this paper, a new methodology is proposed to estimate process capability indices (PCIs) of multivariate nonnormal processes. In the proposed methodology, the skewness of the marginal probability distributions of the variables is first diminished by a root transformation technique. Then, a Monte Carlo simulation method is employed to estimate the process proportion of nonconformities (PNC). Next, the relationship between PNC and PCI is found, and finally, PCI is estimated using PNC. Several multivariate nonnormal distributions such as Beta, Weibull, and Gamma are taken into account in simulation experiments. A real-world problem is also given to demonstrate the application of the proposed procedure. The results obtained from both the simulation studies and the real-world problem show that the proposed method performs well and is able to estimate PCI properly.     

A comparison of two methods for transforming non-normal manufacturing data

Many statistical methods applied to manufacturing quality control and operations management have been under the assumption that the process characteristic investigated is normally distributed. If the process characteristic is not normally distributed, a popular approach is to transform the non-normal data into a normal one. In this paper, we consider the Box-Cox transformation, and compare the transformation power using two different parameter estimation methods, including the maximum likelihood estimator (MLE) and the method of percentiles (MOP). The performance comparison is based on the pass rate under the Shapiro-Wilk normality test. The results show that, in general, the MOP has better pass rate, while the MLE has smaller power variation for most cases investigated. For small sample size (n=5, 10) both methods perform equally well. For large sample size, the MOP is recommended due to its simplicity and significantly higher pass rate.     

The measurement of a process capability for folded normal process data

There are two approaches to estimate the nonconforming percentage and process capability indices which are commonly used to measure the ability of a process to manufacture products meeting a required specification. The most commonly used approaches are based on the assumption that the process is normally distributed. If the process is not normal with a skewed distribution, but normal-based techniques are used to estimate process capability indices or fraction nonconforming, serious errors can result. Basically, non-normal process data, such as folded normal process data, is common in mechanical industries. In this paper, we propose one procedure to evaluate the process capability from folded normal data, by solving the true process mean and process standard deviation of the underlying normal distribution. A numerical result based on the proposed approach is illustrated.     

基于PCI总线控制器的高速大容量实时数据采集

介绍了PCI总线控制器专用芯片S5933的组成、Mailboxes、FIFO和Pass-Thru的基本概念,以及性能和使用方法,并针对高分辨率成像光谱仪数据采集的指标要求,提出了以PCI总线控制器S5933为核心的双路高速、大容量、实时数据采集与实时存储的实现方法,在该方法中通过利用PCI总线控制器的FIFO工作模式以及“双路并行复用”技术,实现了双路数据的实时采集与存储,实验表明数据实时采集与存储速率可达8MB/s,完全满足指标要求,而且具有电路简洁,数据采集速率高和容易扩展等特点.     

A hybrid root transformation and decision on belief approach to monitor multiattribute Poisson processes

Most of industrial applications of statistical process control involve more than one quality characteristics to be monitored. These characteristics are usually correlated, causing challenges for the monitoring methods. These challenges are resolved using multivariate quality control charts that have been widely developed in recent years. Nonetheless, multivariate process monitoring methods encounter a problem when the quality characteristics are of the attribute type and follow nonnormal distributions such as multivariate binomial or multivariate Poisson. Since the data analysis in the latter case is not as easy as the normal case, more complexities are involved to monitor multiattribute processes. In this paper, a hybrid procedure is developed to monitor multiattribute correlated processes, in which number of defects in each characteristic is important. Two phases are proposed to design the monitoring scheme. In the first phase, the inherent skewness of multiattribute Poisson data is almost removed using a root transformation technique. In the second phase, a method based on the decision on belief concept is employed. The transformed data obtained from the first phase are implemented on the decision on belief (DOB) method. Finally, some simulation experiments are performed to compare the performances of the proposed methodology with the ones obtained using the Hotelling T ² and the MEWMA charts in terms of in-control and out-of-control average run length criteria. The simulation results show that the proposed methodology outperforms the other two methods.     

Measurement of synchronous machine parameters using Kalman filter based fuzzy logic estimator

This paper presents a new Kalman filter/fuzzy logic approach for estimating synchronous machine parameters from short circuit tests. The technique uses on-line noisy measurements of the short circuit current for estimating direct axis reactances, and time constant synchronous machine parameters. The approach is based on expressing short circuit current as a discrete time linear dynamic system model suitable for the Kalman filter to estimate the parameters. Fuzzy rule-based logic is used to tune-up measurement noise levels by adjusting the covariance matrix. The results show a better convergence using fuzzy logic than those solely using the Kalman filter.     

Monitoring multivariate–attribute processes based on transformation techniques

Control charts are widely used in monitoring the quality of a product or a process. In most of the cases, quality of a product or a process can be characterized by two or more correlated quality characteristics. Many control charts have been proposed for monitoring multivariate or multi-attribute quality characteristics, separately, but sometimes the correlated variables and attribute quality characteristics represents the quality of a process. In this paper, the use of four transformation methods is proposed to monitor the multivariate–attribute processes. In the first one, the distribution of correlated variables and attribute quality characteristics are transformed to approximate multivariate normal distribution, and then the transformed data are monitored by multivariate control charts including T ² and MEWMA. Based on the second transformation method, the correlated variables and attribute quality characteristics are transformed, such that the correlation between the quality characteristics approaches to zero, then univariate control charts are used in monitoring the transformed data. In the third and fourth proposed methods, a combination of two transformation methods is used to make the quality characteristics independent and to transform them to normal distribution. The difference between the third and fourth method is the order of using the transformation techniques. The performance of the proposed methods is evaluated by using simulation studies in terms of average run length criterion. Finally, the proposed approach is applied to a real dataset.     

Synchronous measurements of the velocity and concentration in low density turbidity currents using an Acoustic Doppler Velocimeter

Low density turbidity currents have been investigated in a laboratory flume. An Acoustic Doppler Velocimeter (ADV) was used to measure the velocity. The dimensionless velocity profiles were compared with previous studies to check the accuracy of acoustic measuring techniques for turbidity currents. Successful use of the ADV to measure the current velocity has led to interest in the technique of using acoustic sensors to estimate concentrations. Acoustic backscattering analyses are used for estimating the sediment concentration in turbidity currents. With this approach, concentration measurements can be reasonably well represented by a similarity profile. Using this technique, an accurate estimation of the concentration close to the bed, where obtaining reliable concentration data by sampling techniques is difficult, is possible. The results show that a power relation is a good estimate for the concentration distribution in this region, for which no reliable expressions have been provided previously.Successful estimation of the velocity and concentration, in the present experiments, indicates that this technique could be appropriate and useful for determining the flow structure in turbidity currents.     

Kinetics of edge crack in the field of residual stresses

A methodical approach and program for calculating the kinetics of flat crack extension with an arbitrary front configuration in the spatial elements of constructions have been developed. The technique creates possibilities to estimate cyclical growth and changes in the front configuration at an arbitrary distribution of stresses. The experimentally calculated method of estimating the crack dimensions has been stated. The example of calculating a crack in the pipeline in the presence of residual stresses and taking into account T stresses is presented.     

A simple numerical procedure for estimating nonlinear uncertainty propagation

Accurately determining the effect of the propagation of uncertainty in nonlinear applications can be awkward and difficult. The Monte Carlo approach requires statistically significant numbers of function evaluations (typically 10⁵ or more) and analytical methods are intractable for all but the simplest cases. This paper derives and demonstrates a method to estimate the propagation of uncertainty in nonlinear cases by representing the function in a piecewise fashion with straight line segments. The probability density function of the result can be calculated from the transformation of the line segments. The mean and confidence intervals of the result can then be calculated from the probability density function. For the special case of a normal distribution in the independent variable, calculation of the mean and confidence intervals requires evaluation of only the error function (erf). A simple example is presented to demonstrate the technique. Variations on the basic approach are presented and discussed.     

A new and simple method to construct root locus of general fractional-order systems

Recently fractional-order (FO) differential equations are widely used in the areas of modeling and control. They are multivalued in nature hence their stability is defined using Riemann surfaces. The stability analysis of FO linear systems using the technique of Root Locus is the main focus of this paper. Procedure to plot root locus of FO systems in s-plane has been proposed by many authors, which are complicated, and analysis using these methods is also difficult and incomplete. In this paper, we have proposed a simple method of plotting root locus of FO systems. In the proposed method, the FO system is transformed into its integer-order counterpart and then root locus of this transformed system is plotted. It is shown with the help of examples that the root locus of this transformed system (which is obviously very easy to plot) has exactly the same shape and structure as the root locus of the original FO system. So stability of the FO system can be directly deduced and analyzed from the root locus of the transformed IO system. This proposed procedure of developing and analyzing the root locus of FO systems is much easier and straightforward than the existing methods suggested in the literature. This root locus plot is used to comment about the stability of FO system. It also gives the range for the amplifier gain k required to maintain this stability. The reliability of the method is verified with analytical calculations.     

极小子样可靠性评定方法

针对贵重产品可靠性评定中只有极少几个试验数据的情况,提出一种极小子样可靠性评定方法。该方法能将仿真(或数字化设计)结果与试验数据有机结合,实现多个不同状态下(多个母体)试验数据的整体推断,从而建立各状态下均值和方差的估计量,给出母体百分位值和百分率的置信限和置信区间估计。与传统方法相比,该方法具有更高的精度,解决了一种状态下只有一个试验数据时产品可靠性评定的难题。     

Flow resistance due to shrubs and woody vegetation

In this paper, a theoretical open channel flow resistance equation was verified using flow depth and discharge measurements carried out by Freeman et al. in a large channel, 2.44 m wide, for ten different types of uniform-sized plants (shrubs and woody vegetation). The plants, which are broadleaf deciduous vegetation commonly found in floodplains and riparian zones, were placed in staggered rows inside the channel whose bed was constructed to accept plants with their root systems. For each species, the available measurements were carried out by Freeman et al. with plants having different values of plant density, height, and bending stiffness. The available literature database (87 measurements) was divided into two groups which were separately used to calibrate and test the theoretical approach. In particular, 46 measurements were used to calibrate the relationship between the scale factor Γ of the velocity profile, the Froude number, and the channel slope. This relationship was calibrated using the entire available dataset or varying the scaling coefficient a with the investigated vegetation type. The measured values of the Darcy-Weisbach friction factor, obtained by the measured flow velocity, water depth and slope values, were compared with those calculated by the theoretical flow resistance law, coupled with the relationship for estimating the Γ function having a scaling coefficient different for each investigated vegetation type. This comparison allowed to demonstrate that an accurate estimate of the Darcy-Weisbach friction factor (errors less than or equal to ±10% for 87% of the investigated cases) can be obtained. However, for the investigated vegetation species, that are characterized by a large range of bending stiffness, also a mean value of the scaling coefficient a equal to 0.3283 allows an accurate estimate of the Darcy-Weisbach friction factor.     

含非正态随机参数结构可靠度的虚拟变量算法

虚拟变量法是一种把随机问题转化确定性问题的方法,基于与失效函数有关的随机结构变量的均值,标准差和概率分布统计参数的确定性特征,本文提出了按一次二阶矩理论准确计算含非正态随机参数结构可靠度的一种虚拟变量算法。     

非正态分布参数的车辆零件可靠性稳健设计

将可靠性优化设计理论、可靠性灵敏度技术与稳健设计方法相结合,讨论了具有非正态分布参数的车辆零件的可靠性稳健设计问题,提出了可靠性稳健设计的计算方法。把可靠性灵敏度溶入可靠性优化设计模型之中,将可靠性稳健设计归结为满足可靠性要求的多目标优化问题。在基本随机参数的前四阶矩已知的情况下,通过计算机程序可以实现具有非正态分布参数的车辆零件的可靠性稳健设计,迅速准确地得到具有非正态分布参数的车辆零件的可靠性稳健设计信息。数值算例表明所提出的方法是一种非常方便和实用的可靠性稳健设计方法。     

基于PCI和DSP结构的多通道神经信号采集系统的研制

详细阐述了一种基于DSP和PCI结构的多通道神经信号高速数据采集系统的设计与实现。该系统采用CPLD实现了DSP与多通道ADC的逻辑和时序控制，通过DSP的HPI与PCI总线接口设计实现了采集数据的高速传输。本文还介绍了Windows 2Z000下运用DriverStudio软件开发基于WDM的PCI设备驱动程序的相关技术。最后给出了系统应用于大鼠脑电信号采集的实验结果。     

Using nonstationary season autoregressive integrated moving average models in resource saving problems

A technique for identifying and estimating the parameters of a multiplicative autoregressive integrated moving average model is considered. The model is used for describing homogeneous nonstationary processes with season variations. The technique is based on selective sounding the space of model parameters. The approach differs from the one that usually uses correlation and partial correlation functions. It enables working with large-dimension models and a great number of seasons. Forecasting the energy resource consumption is illustrated.     

Regressing sample quantiles to perform nonparametric capability analysis

Capability analysis corresponds to a set of methods used to estimate and test the ability of an in-control process to provide a specific output. When there is only one quality characteristic that behaves as a continuous random variable, indices like C_p and C_pk can be used to measure how well requirements are met. Under normality, variation is indicated using 3?sigma limits; otherwise, the corresponding quantiles are used. Distribution fitting and transformations to normality can be used to estimate quantiles by finding an overall fit to the data available. However, by giving the same weight to all observations, the best possible fit of extreme values can be lost. To address this issue, a regression approach is proposed to fit functions over maximum likelihood estimates of probabilities of extreme values. A case study from the automotive industry is used to illustrate the proposed approach. To evaluate the performance, extensive Monte Carlo simulation is used, and the results are compared with the corresponding approach using the Clements method. The proposed nonparametric technique shows smaller MAD when high levels of skewness exist. Practitioners with basic knowledge of regression analysis may find the approach useful to estimate capability indices without the need of a specific probability distribution.     

Estimation of one-dimensional velocity distribution by measuring velocity at two points

The measurement of the velocity distribution and discharge in the open channels has always been an important issue in hydraulics. Unfortunately, flow measurement in the open channel is often expensive and sometimes produces poor results. There are many empirical methods to estimate the velocity distribution in a conduit, however, these methods are often applicable only to a narrow range of open channel conditions. In this paper, considering velocity as a random parameter, one-dimensional velocity distribution in open-channel has been derived based on the entropy concept and the principle of maximum entropy (POME). The entropy indexes (M, G, λ₂ and λ_*) are important parameters in entropy method to estimate velocity distribution and discharge in a conduit. A new approach is presented in this work for estimating the entropy parameters based on two-point velocity measurements. The approach for estimating the entropy parameters is tested for laboratory observations and velocity distribution and discharge are determined using Shannon, Renyi and Tsallis entropy methods. The present approach has shown good agreement with measured data. Also, the results showed that Tsallis entropy method is more accurate than other forms of entropy and the calculated values of NRMSE for estimated velocity profile and discharge are 7.86 and 8.8% respectively, showing a good simulation.     

A method of functional invariants in problems of strength assessment based on acoustic emission

A new technique is developed to estimate the dynamics of developing acoustic emission (AE) processes on the basis of phenomenological models and experimental data through detection of a disturbance of the Poisson distribution of AE pulses that indicates the onset of development of a structural fracture process. This approach has some advantages due to processing of amplitude information.