Research On The Condition Monitoring And Fault Diagnosis Of Electric-Mechanical Equipments Face To Plant Management

The dynamic signals of mechanical equipment often possess nonstationarities and strong noise due to occurrence of fault, variance of operation and inherent nonlinearity of equipment, which brings difficulties to fault diagnosis. Aiming at the electric- mechanical equipment, this dissertation focuses on the theroy and applications of time-frequency feature extraction for complex signals and intelligent fault diagnosis mentod for rotating machinary.Aiming at the non-stationary signals in engineering, the original vibration signal was preprocessed by singular value decomposition (SVD) to reduced noise, the influence induced by singularity data or high frequency noise was restrained, and the cleaning signal was decomposed by EMD to extract the intrinsic mode functions (IMFs). The results show that SVD can effectively increase the signal noise ratio (SNR) and emphasize the fault characteristic of the original vibration signal, the IMFs extracted from the denoised signal have clear physical meaning and will increase the precision of fault information. The insufficiency of phase demodulation method based on Hilbert transformation was researched, and a new demodulation method based on EMD was introduced . Two limitations of this method when used in fault diagnosis of mechanical equipments are proposed.Aiming at the mixing signals in engineering measurement, mixing model and separation method in common use are summarized. The combined ICA-HHT method is applied in the sensor failure detection and incipient fault diagnosis of rotor system. The results show that this method can extract the fault information from mixed vibration signals, and distributed in the HHT spectrum. The feasibility of the blind separation for mechanical vibration signals in transformation domain is demonstrated. Successful applications of BSS are achieved in the detection of eddy-current sensor failure and the diagnosis of incipient impact-rub fault. The results show that BSS has widely prospect for application in the condition monitoring and fault diagnosis of mechanical equipment, and transcendental knowledge of equipment's vibration are helpful for us to analyse the independent components.In order to achieve the intelligent fault diagnosis, aiming at some common vibration failure in rotary machine, vibration features were described and the basic theory on Bayesian Network was elaborated. The accurate reference approaches based on junction trees and some learning methods were discussed, and the mechanical failure diagnosis model based on Bayesian Network was established. The result of an application of this model in engineering shows the validity of this method.The automatic diagnosis mehtod based on vibration information was proposed. The acduisition of spectrum feature of fault pattern class by the fuzzy clustering algotithm is the bisis of pattern recognition in the level of fault class. The result acduired by fuzzy relationship between vibration symptom and fault shows the feature of fuzzy-relation-based method. A kind of fuzzy fault diagnosis expert system of multiple symptoms based on Clips was introduced. The expression of multiple symptom fuzzy diagnosis knowledge and subordinate degree of rules was discussed.Finally, the precept, the system truss and prototype of"Plant Management Information System based on Internet and Condition Monitoring(PMIS)"are proposed to eliminate the"information island"between the plant management and condition monitoring. the integration technologies between different modules and different systems are discussed. A perfect PMIS is built and applied in the Dagang Power Plant.