Rapid unit failure diagnosis method based on full state information

Abstract

The invention discloses a rapid unit failure diagnosis method based on full state information. The method includes the steps that a probabilistic neural network is used for conducting unit failure recognition; 14 types of typical failure data are used for obtaining the characteristic values of all the 14 types of failure through holographic spectral analysis or time domain statistic analysis, and all the characteristic values constitute characteristic vectors; the 14 types of sample characteristic vectors are used as the weight vectors Wj of 14 types of mode units respectively; the data to be diagnosed are selected to conduct the holographic spectral analysis so that the characteristic vectors can be obtained to be used as the input vectors Xj of a neural network input layer, and input sample data and training sample data adopt the same parameter; scalar product calculation is carried out on the input vector Xj and the weight vector Wj of each mode unit; summation of the outputs g(Zj) of the mode units corresponding to the same failure mode is conducted so that the probability density of the failure can be estimated; the outputs fR(X) of 14 accumulation layers corresponding to the 14 types of failure modes are used as inputs so that the failure modes can be judged through the Bayes judgment strategy. By means of the rapid unit failure diagnosis method based on the full state information, the expertise in the field can be fully utilized, and the requirement for the experience of a user himself or herself is lowered.

Description

technical field [0001] The invention relates to a fault diagnosis method for a unit. In particular, it relates to a rapid diagnosis method for unit faults based on full state information. Background technique [0002] Among the many diagnostic methods of rotating machinery, whether it is time-domain analysis, spectrum analysis, or holographic spectrum analysis, rich experience and professional knowledge are required, and the operator's technical level is relatively high. Most of the existing condition monitoring and diagnosis systems only provide some means of analysis, and professional technicians are needed to draw conclusions. However, the development of computer and artificial intelligence provides a new direction for the fault diagnosis of rotating machinery. The artificial intelligence method is introduced into the field of fault diagnosis of rotating machinery, and the traditional diagnosis method is combined with artificial intelligence technology, which can make f...

AI Technical Summary

Problems solved by technology

In addition, because the traditional state assessment method based on holograms is too dependent on experience, the traditional state monitoring and fault diagnosis of rotating machinery requires high user experience, which limits the development of fault detection and diagnosis technology.

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