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首页> 外文会议>ASME Power Conference 2005: Includes Papers from the 2005 International Conference on Power Engineering(ICOPE) pt.A >Study on Self-Learning for Vibration Fault Diagnosis System of Rotating Machinery
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Study on Self-Learning for Vibration Fault Diagnosis System of Rotating Machinery

机译：旋转机械振动故障诊断系统自学习研究

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For an established fault diagnosis system which is based on it own expert system, it is usually incapable to diagnosis the new operating conditions, of which the knowledge has not been explored by the system. It is the purpose of the paper to develop the approach of identifying new fault and self-learning for diagnosis based on non-linear fractal theorem. It has been generally accepted that the vibration series has obvious fractal feature, which can reflect the essential characteristics of new fault. When the novel fault is taken place in the system, a related sub-net is increased to the system and trained with mis sample. We have verified experimentally that the fractal dimensions of the same class faults are distributed approximately around a definite value mat can represents the dimension of the standard sample for the novel fault. Based on non-linear theorem, the approach of identifying new fault and self-learning for diagnosing is put forward.
机译：对于基于其自身专家系统的已建立的故障诊断系统，通常无法诊断新的运行状况，而系统尚未对此知识进行探索。本文的目的是开发一种基于非线性分形定理的新故障识别和自学习诊断方法。振动序列具有明显的分形特征，可以反映新断层的本质特征，这已被普遍接受。当系统中发生新的故障时，会将相关子网添加到系统中，并使用错误的样本进行训练。我们已经通过实验证明，同一类断层的分形维数大约在一个确定值垫周围分布，可以代表新型断层的标准样本的维数。基于非线性定理，提出了新故障的识别和自学习的诊断方法。

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