【摘要】 对于一个已建立起来的故障诊断系统 ,其识别诊断故障的能力取决于诊断系统的知识容量。然而 ,人们对故障机理的认识是有限的 ,诊断系统不可能覆盖所有可能发生的故障 ,因此 ,对某些异常现象无法作出准确的辨识。文章着重研究振动故障的非线性特征 ,通过计算振动故障的分形维数提取新故障样本 ,提出了一种诊断能力自我扩充的理论方法 ,并进行了实例验证更多还原

【Abstract】 For a given diagnosis system, its diagnosis ability lies on the knowledge capacity. It is incapable to detect a new fault condition if no priori knowledge is given. We divide the conventional networks into several sub nets, which is responsible for one specific fault class. The vibration series has obvious fractal feature. It can reflect the essential characteristics of new fault. When the new fault is taken on, a new sub net is increased and trained with the sample. If other samples are identified as this new class according to proximity, it has been verified experimentally these fractal dimensions of one class are distributed approximately around a definite value that 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.更多还原