Measurement selection for parametric IC fault diagnosis

This article presents experimental results which show feedforward neural networks are well-suited for analog IC fault diagnosis. Boundary band data (BBD) measurement selection is used to reduce the computational overhead of the FFN training phase. We compare the diagnostic accuracy between traditional statistical classifiers and feedforward neural networks trained with various measurement selection criteria. The feedforward networks consistently perform as well as or better than the other classifiers in term of accuracy. Training using BBD consistently reduces the FFN training efforts without degrading the performance. Experimental results suggest that feedforward networks provide a cost efficient method for IC fault diagnosis in a large scale production testing environment.This work is supported by NSF-IUC CDADIC, Project 90-1.     

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。     

基于B点磁探针膛内电枢电弧长度与速度分布测量的计算机控制系统的研究

基于B点磁探针的测量原理，建立了电磁轨道炮膛内三维等离子体电枢电弧长度和电弧运动速度测量的数学模型，通过对该模型的分析给出了用于测量膛内电枢电弧长度和运动速度分布的相应推论。开发了用于测量膛内等离子体电枢电弧长度和速度的多通道计算机控制诊断系统，并进行了相应的试验研究。     

Role of the Basal Ganglia in Category Learning: How Do Patients With Parkinson's Disease Learn?

The purpose of the present study was to gain a deeper understanding of the role of the basal ganglia in learning and memory by examining learning strategies among patients with basal ganglia dysfunction. Using a probabilistic category learning task (the "weather prediction" task) previously shown to be sensitive to basal ganglia function, the authors examined patterns of performance during learning and used mathematical models to capture different learning strategies. Results showed that patients with Parkinson's disease exhibit different patterns of strategy use. Specifically, most controls initially used a simple, but suboptimal, strategy that focused on single-cue-outcome associations; eventually, however, most controls adopted a more complex, optimal learning strategy, integrating single-cue associations to predict outcomes for multiple-cue stimuli. In contrast, the majority of individuals with Parkinson's disease continued to rely on simple single-cue learning strategies throughout the experiment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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 .     

Empirical assessment of a threshold model for sylvatic plague.

Plague surveillance programmes established in Kazakhstan, Central Asia, during the previous century, have generated large plague archives that have been used to parameterize an abundance threshold model for sylvatic plague in great gerbil (Rhombomys opimus) populations. Here, we assess the model using additional data from the same archives. Throughout the focus, population levels above the threshold were a necessary condition for an epizootic to occur. However, there were large numbers of occasions when an epizootic was not observed even though great gerbils were, and had been, abundant. We examine six hypotheses that could explain the resulting false positive predictions, namely (i) including end-of-outbreak data erroneously lowers the estimated threshold, (ii) too few gerbils were tested, (iii) plague becomes locally extinct, (iv) the abundance of fleas was too low, (v) the climate was unfavourable, and (vi) a high proportion of gerbils were resistant. Of these, separate thresholds, fleas and climate received some support but accounted for few false positives and can be disregarded as serious omissions from the model. Small sample size and local extinction received strong support and can account for most of the false positives. Host resistance received no support here but should be subject to more direct experimental testing.     

冶钢小方坯连铸机结晶器液面控制系统故障分析及对策

本文针对某一钢铁厂的一套小方坯连铸机结晶器液面自动控制系统，对其故障原因进行了深入分析，指出导致系统控制性能达不到设计要求长期无法投入运行的主要原因，并提出了相应的解决方法。     

喷气客机涡轮风扇发动机的振动诊断与改进

通过动态分析和现场测试对一喷气客机的发动机振动作出诊断，发现涡轮叶轮的变柔导致其极转动惯量显著下降，因而改变发动机的动态特性，使其临界转速接近飞机的巡航转速，产生不应有的振动和噪声。文章提出了具体改进方案，已为研制单位采用。