A novel distributed approach to robust fault detection and identification

In this paper, we propose a novel distributed robust fault detection and identification (RFDI) scheme for a class of nonlinear systems. Firstly, a detection and identification estimator—robust fault tracking approximator (RFTA) is designed for online health monitoring. A novel feature of the RFTA is that it can simultaneously detect and accurately identify the shape and magnitude of the fault and disturbance. Moreover, it takes less online training time compared with the traditional neural network based fault diagnosis schemes. For some distributed systems, a network of distributed estimators is constructed where the RFTA is embedded into each estimator. Then we use consensus filter to filter the outputs of each estimator. One of the most important merits of the consensus filter is that its outputs can dramatically improve the accuracy of fault detection and identification. Next, the stability of the distributed RFDI scheme is rigorously investigated. Finally, two numerical examples are given to illustrate the feasibility and effectiveness of the proposed approach.     

On the binomial confidence interval and probabilistic robust control

The Clopper-Pearson confidence interval has ever been documented as an exact approach in some statistics literature. More recently, such approach of interval estimation has been introduced to probabilistic control theory and has been referred as non-conservative in control community. In this note, we clarify the fact that the so-called exact approach is actually conservative. In particular, we derive analytic results demonstrating the extent of conservatism in the context of probabilistic robustness analysis. This investigation encourages seeking better methods of confidence interval construction for robust control purpose.     

Adaptive switching supervisory control of nonlinear systems with no prior knowledge of noise bounds

The problem of controlling nonlinear noisy systems affected by parametric uncertainties is approached via the introduction of a supervisor which, whenever needed, switches on, in feedback to the plant, a controller selected from a finite set of predesigned controllers. A Lyapunov-based falsification criterion allows one to ensure robust stability in the presence of uncertain constant parameters and exogenous bounded disturbances. Simulations are discussed in order to illustrate the merits of the proposed algorithms.     

PDC钻头在东海西湖凹陷的实践与认识

文章针对在东海西湖凹陷六口井 31 1 .2mm及 2 1 5 .9mm井眼所使用的PDC钻头统计分析 ,并结合笔者自己使用PDC钻头的实践 ,提出使用PDC钻头时注意环节 ,建议针对西湖凹陷地层的特点选择适合该地区的PDC钻头类型     

基于Delphi的报童模型的实现与动态仿真

已往库存论中的决策模型,它是把现实过程用数学的方法进行分析,得到决策值,但不能看到决策的过程,不能反映出现实生活中的不确定性、动态性。运用Delphi 7.0和SQL server 2000对销售过程进行了动态仿真,利用Newsboy模型的推广(S,s)订购策略来监控库存并做出决策,这样就体现出此过程的不确定性、动态性;且举出了一个仿真商品销售的实例,对其中的一些关键技术,如何仿真、算法的实现等进行了阐述。     

Generalization of the Gram-Charlier/Edgeworth Series and Application to Time-Frequency Analysis

We derive a general equation relating probability densities and as special cases we the obtain Gram-Charlier and Edgeworth series. This allows us to generalize these methods and clarify a number of issues pertaining to both probability theory and time-frequency analysis. In particular we show how the Gram-Charlier and Edgeworth series are related to the kernel method of time-frequency analysis. The approach allows us to construct densities that satisfy given constraints such as joint moments or conditional moments. Also, we show that the kernel has to be signal dependent and that to obtain a proper distribution it should be the ratio of two characteristic functions.     

注聚采出水分离用动态水力旋流器特性研究

原有的地面工艺和设备已不能满足聚合物驱采出水分离的要求。采用自行研制的动态水力旋流器，在增压与不增压两种工艺流程下，分别进行了流量和转数对分离效率的影响、转数与压力降的关系，以及入口压力与处理量的关系等项试验研究。试验结果表明，与静态水力旋流器相比，动态水力旋流器的分离效率明显提高，一般可提高10％左右；压力损失显著降低，约可减少0．1MPa，足见可在低压下工作，易于实现无泵运行；无泵运行明显比有泵增压运行的分离效率高，但处理量会有所降低。将动态水力旋流器应用于聚合物驱产出水的油－水分离处理是完全可行的。     

发动机扭矩与转速关系经验公式试验研究

在对发动机外特性曲线扭矩与转速实验数据分析的基础上，对原有的经验公式进行了分析、改进，提出了新的经验公式．由这一经验公式所计算的数值，在最大扭矩与最大功率点之间，可与实验数据拟合得很好．与原经验曲线相比，新经验曲线与实验曲线的离差可下降50％以上．     

A review of robust design methods for multiple responses

Problems in engineering design often involve determining design variable settings to optimize individual product performance for multiple criteria, which are often in conflict. We review mathematically rigorous techniques from the statistical literature for finding a vector x of design variable settings, which produces an optimal compromise solution among a group of prioritized response variables. The best compromise solution is typically gained by optimizing an objective function, which incorporates the prioritized demands of multiple responses. Since most multi-response objective functions are constructed by combining the functions used to optimize univariate responses, a review of the prominent univariate approaches is presented first. A multivariate approach from the engineering literature called the compromise Decision Support Problem is also reviewed. Finally a table comparing the relative merits of the different multivariate approaches summarizes the article in a concise and user-friendly fashion.