Rotary machine failure intelligent diagnosis method and device

Abstract

The system comprises preprocessing board connecting key phase signal to fast signal, key phase board controlling whole period of sampling, high speed collecting board for vibration signal, low speed data collecting board for graded signal. In the method, they are connected to down level server to fulfil data collection, signal analysis, and automatic identification as well as picking up characteristic parameters of operatino of machine set and down level server can make high speed communication with up level server through network card or IP protocol as up level server is equipped with an intelligent reasoning machine integrating professional knowledge rule, fuzzy logic and neural network in one component to carry trend prediction and intelligent diagnosis for operation and failure of machine.

Description

Technical field: [0001] The invention relates to an intelligent fault diagnosis method and device for rotating machinery. Background technique: [0002] With the rapid development of science and technology, large-scale rotating mechanical equipment such as steam turbines, generators, blowers, and compressors are becoming larger, faster, and more complex, and the possibility or probability of failure increases. Therefore, it is of great practical significance to predict the deterioration trend of rotating machinery equipment in time, carry out intelligent diagnosis and prediction of faults and deal with them quickly, and gradually realize remote diagnosis. [0003] At present, foreign fault diagnosis systems include CSI2115 / 1900 / 2000 from CSI, CMVA40 / 20 / 10 from SKF-CM, Suapshot from Bently, data PAC1500 from Entek-IRD, EDL (EMONITOR dataline), EBL (EMONITOR, baseline ) HP3560A of HP Company, BK2515 of Danish B&K Company, and PL22 / 202 / 302 online diagnostic system of British D...

AI Technical Summary

Problems solved by technology

However, in the diagnosis methods of these systems, the location of the equipment failure and the cause of the failure must be judged manually based on the experience of experts; the "bottleneck" of knowledge expression and utilization, the flexibility of the human-machine interface is poor; the system's poor learning ability cannot effectively evaluate, improve, and expand experts. Knowledge; Most do not have artificial intelligence, lack of interpretation of test results, etc.

"On-line status monitoring and fault diagnosis system for large rotating units" (ZL97102397.2) proposes an online status monitoring and fault diagnosis system for large rotating units consisting of a signal preprocessing board, a high-speed data acquisition card, and an upper and lower computer, which can realize For data collection, monitoring and fault diagnosis, the system is still judged manually based on the experience of testers, without corresponding expert knowledge base and intelligent reasoning machine, and does not have intelligent diagnosis function

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