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.     

Ultrasound‐activated Knoevenagel condensation of malononitrile with carbonylic compounds catalysed by alkaline‐doped saponites

α,β‐Unsaturated nitriles have been synthesized by Knoevenagel condensation of a carbonylic compound with malononitrile, assisted by sonochemical irradiation. Two alkaline‐promoted clays (Li⁺‐ and Cs⁺‐exchanged saponites) have been employed as catalysts. The influence of the carbonylic compound (benzaldehyde or cyclohexanone) and the use of a solvent on the catalytic activity have been studied. Remarkable increase in the conversion values has been found when the reaction is activated by ultrasound, as compared with the thermal activation. In this green, solvent‐free procedure, α,β‐unsaturated nitriles have been produced in very high yields (97%) when the Cs⁺‐saponite is used as catalyst. Copyright © 2004 Society of Chemical Industry     

改进的第三代相干算法及应用

在讨论相干技术发展及三代相干算法优缺点的基础上，介绍了改进的第三代相干算法。应用自行开发的相干软件对多个油气勘探和开发区块的三维地震资料进行处理、分析，对相干时窗长度、相关道数等参数对算法效果的影响及相干体对断层、异常地质体的识别，以及解释方法进行了探讨。应用实例证明，改进的第三代相干算法抗干扰能力强，横向分辨率高，不仅适用于倾斜地层，而且对断层尤其对小断层、异常地质体具有较高的检测能力，是油气勘探和开发的一项有效和实用的技术。     

巯基棉预浓集—中子活化法测定水泥及其浸出水中的微量汞

在不同酸度下巯基棉可以从溶液中富集和分离Au、As、Ag、Br、Cu、Ca Co、InHg、Ni、Se、Sb、Sn、Pb、W、Fe和In等30多种元素。由于用巯基棉吸附待测元素后不必洗脱,因此大大提高了富集能力,可用于海水、地下水、地表水及工业废水中一些元素的测量。本文用巯基棉富集后进行中子活化分析测定水泥及其浸出水中的汞。     

Test Generation for Crosstalk-Induced Faults: Framework and Computational Results

Due to technology scaling and increasing clock frequency, problems due to noise effects lead to an increase in design/debugging efforts and a decrease in circuit performance. This paper addresses the problem of efficiently and accurately generating two-vector tests for crosstalk induced effects, such as pulses, signal speedup and slowdown, in digital combinational circuits. These noise effects can propagate through a circuit and create a logic error in a latch or at a primary output. We have developed a mixed-signal test generator, called XGEN, that incorporates classical static values as well as dynamic signals such as transitions and pulses, and timing information such as signal arrival times, rise/fall times, and gate delay. In this paper we first discuss the general framework of the test generation algorithm followed by computational results. Comparison of results with SPICE simulations confirms the accuracy of this approach.     

硫脲型整合树脂在分析上的应用 Ⅰ.岩样中微量银的中子活化分析

前言银和其他贵金属元素一样,在地壳中比较稀少,按其自然丰度为第67号元素,平均含量仅0.07 ppm。由于地质样品成分非常复杂,为测定其微量银,必须进行严格的化学分离。但是迄今文献中报道的许多分离分法,步骤都相当冗长,一般采用氯化银反     

基于故障树与灰关联分析的鱼雷故障诊断

将灰色关联度与故障树联合运用于鱼雷故障诊断,为鱼雷的故障诊断提供了一种新途径。在鱼雷动力系统故障树诊断模式识别中,运用灰色系统理论中的灰关联分析,通过进行关联度计算及排序,对各种故障模式发生的可能性大小作出了判断,从而为处理故障的轻重缓急、控制故障的发生以及改进系统的可靠性提供了理论依据。实例诊断表明,这种方法是有效可行的,并且诊断结果可靠。     

Application of the blind source separation method to feature extraction of machine sound signals

As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semi-anechoic chamber demonstrate the effectiveness of the presented methods .     

环缝错边容器的有限元分析与容限探讨

采用有限元方法对 1台封头与筒体具有较大错边量的容器进行了应力分析 ,得到了错边区域的应力分布状况 ,并对其进行应力强度评定。结果表明 ,该容器错边部位的应力是满足应力分析准则的。通过对存在不同错边量的容器进行应力分析 ,探讨了容器的错边容限 ,对含有错边缺陷在役容器是否合乎使用的判断具有一定指导意义