| 1. | In this paper we will present and diagnosis procedures for general interconnect faults in fpgas
并着重说明了所有单故障和多故障的具体情况及适用的诊断策略。 |
| 2. | By using this method the multi - fault diagnosis problem of turbine machinery is solved
设计了遗传神经网络故障诊断模型，用这种新方法解决了汽轮机多故障诊断问题。 |
| 3. | ( 9 ) a method , which combines the neural network and the genetic algorithms , is advanced . it uses the genetic algorithms to improve the weight and threshold of the neural network first
（ 9 ）提出了以遗传算法对神经网络的权值和阈值进行优化并用神经网络细调的多故障诊断方法。 |
| 4. | At the same time , steam turbine generator - set is a system , which often has multiple faults synchronously , and the problem for the multiple faults has not been solved appropriately
同时，由于汽轮发电机组是一个多故障系统，经常出现多种故障同时发生的情况，而目前对于多故障的诊断问题还未能很好的解决。 |
| 5. | ( 5 ) a two - layer neural network model for steam turbine generator set ' s multiple fault diagnosis is put forward . it can identify the multiple faults and has high modularization . also it can find the new fault
( 5 )建立了汽轮发电机组多故障诊断的两级神经网络模型，它不仅对多故障诊断识别精度高和模块化程度高，且具有发现新故障的能力。 |
| 6. | Different power supply leads to different networks . these networks are parallel . two algorithms are proposed to combine the network diagnosing faults . the system is capable ofdiagnosising multiple faults even if trained on single ones
本文所设计的神经网络故障诊断系统即使在训练单个故障模式情况下也能进行多故障诊断,这是网络泛化能力的体现。 |
| 7. | With regard to sophisticated mechanical equipment having much fault ~ much process and sudden fault , the traditional diagnosis technology and theory method often show more limitations , so the mechanical fault diagnosis is found in some difficult position
传统的诊断技术和理论方法对于具有多故障、多过程、突发性故障的现代化机械设备，往往显示出较大的局限性，从而使机械故障诊断陷入了某些困境。 |
| 8. | ( 3 ) the probability causal model and the connective model based on the probability causal for the fault diagnosis of steam turbine generator - set are put forward by the analysis of the relationships between the probability causal model and the parsimonious cover set theory
( 3 )通过概率因果模型与节约覆盖集理论关系的分析，建立了一种用于汽轮发电机组振动多故障诊断的概率因果模型和基于概率因果的联接模型。 |
| 9. | Then using the neural network , the diagnosis result can be gotten . the methods and models that this paper put forwards can diagnose the vibration multiple faults of steam turbine generator - set effectively , and this could be proved by examples . it enriches the theory and methods in a cert
本文提出的上述模型及方法，经实例分析验证，可对汽轮发电机组振动多故障进行有效诊断，在一定程度上丰富了机组多故障诊断技术的理论及方法，有良好的应用前景。 |
| 10. | The research work presented a large quantity of debris characteristics parameters , and especially made a thorough study on the characteristic description of sediment chain graph ; meanwhile , the sensibility , differentiation and information redundance analyses of the characteristic parameters also supplies the quantitative indexes for the filtration and optimization of the debris characteristic parameters ; in addition , the debris fusion decision recognition method based on the proof fusion theory and the comprehensive debris recognition flow provide reliable recognition arithmetic for debris recognition ; and lastly , the fault fusion diagnosis judging method based on ferro - graph and spectral analysis provides the basic diagnosis method in theory for multi - fault premonitory diagnosis system of aero - engine
本文研究工作提出了大量磨粒特征参数，尤其深入研究了沉积链谱片的特征描述问题；同时，特征参数的敏感性、区分度与冗余度分析为磨粒特征参数的筛选优化提供了量化指标；另外，基于证据融合理论的磨粒统计融合决策识别方法以及磨粒综合识别流程为磨粒识别提供了可靠的识别算法；最后，基于光谱和铁谱信息的磨损故障融合诊断决策方法为发动机多故障征兆综合诊断系统提供了基本的诊断理论手段。 |
