Rotating machinery vibrating failure diagnosis device and method

Abstract

The method comprises: using a sensor to collect the characteristic signals; the collected signals about each kind of running state are sent to the data processing module; after processed, the signals are sent to the computer system; said computer system comprises a device database, a symptom event database, a diagnosis knowledge database; a device database and signal analysis module, a symptom acquiring module, a diagnostic reasoning module, and a diagnosis explaining module; the software of the data processing module makes data exchange; the symptom event database exchanges data with the diagnostic module, data processing module; the diagnostic knowledge exchanges the data with the failure processing module, knowledge acquiring module and data processing module.

Description

technical field [0001] The invention relates to the field of equipment fault diagnosis, in particular to the system diagnosis technology for the vibration fault of rotating machinery by using computer software. Background technique [0002] Since the 1980s, with the development of artificial intelligence technology, expert system technology has been practically applied in the field of vibration fault diagnosis of rotating machinery. The artificial intelligence diagnosis system for turbine generator sets (including three artificial intelligence online diagnosis systems for steam turbine, generator and water chemical treatment, namely TurbinAID, GenAID, and ChemAID), is an engineer assistance system (Engineer Assist) launched by Bentley in 1993. Some universities and research institutes in our country have also carried out the development of fault diagnosis expert systems, some of which have been put into operation. At present, the diagnostic knowledge base capacity of most d...

AI Technical Summary

Problems solved by technology

At present, the diagnostic knowledge base capacity of most domestic diagnostic systems is small, and the feature extraction ability is weak. They mainly use dialogue to obtain fault symptoms, which reduces the accuracy and real-time performance of fault diagnosis.

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