Papers by Author: Wen Xiu Lu

Abstract: Based on pattern spectrum entropy and proximal support vector machine (PSVM), a motor rolling bearing fault diagnosis method is proposed in this paper. It is very difficult to filter the fault vibration signals from the strong noise background because the roller bearing fault diagnosis is a problem of multi-class classification of inner ring fault, outer ring fault and ball fault. Firstly, vibration signals are processed by the pattern spectrum. Secondly, the morphological pattern spectrum entropy, and pattern spectrum values are utilized to identify the fault features of input parameters of PSVM classifiers. The experiment results demonstrate that the pattern spectrum quantifies various aspects of the shape-size content of a signal, and PSVM costs a little time and has better efficiency than the standard SVM.

Abstract: An experimental setup of rotor-bearing system is installed and vibration characteristics of the system with pedestal looseness are investigated. The pretightening bolt between the bearing house and pedestal is adjusted to simulate the pedestal looseness fault. The vibration waveforms, spectra and orbits are used to analyze the nonlinear response of the system with pedestal looseness. Different parameters, including speed, looseness gap, imbalance mass and disk position are changed to observe the nonlinear vibration characteristics. The experiments show that the system motion generally contains the 1/2X fractional harmonic component and multiple harmonic components such as 2X, 3X, etc. Under some special conditions, the pedestal looseness occurs intermittently, that is, occurs in some periods and doesn’t in other periods.

Abstract: The rubbing fault is a very serious and frequent malfunction in rotating machinery, and the determination of the rubbing location is very important in actual fault diagnosis. In this paper, a method based on genetic algorithms to detect the rubbing location is presented. The finite element model of the rubbing rotor is established with the rubbing location, the stator stiffness, the clearance between stator and rotor, the damping coefficient and the friction coefficient as the fault parameters, and the rubbing location determination is transferred into the parameter identification problem. The genetic algorithm is then utilized to search the solution. Using genetic algorithms avoids some of the weaknesses of traditional parameter identification methods such as local minimum problem in nonlinear system identification. The experimental results suggest that the rubbing location can be effectively determined when the rubbing occurs.

Abstract: The steam turboset is the key equipment of the electric power system. Thus, it is very important and necessary to monitor and diagnose the running condition and the faults of the steam turboset for the safe and normal running of the electric power system. In this paper, the Internet/Intranet based remote condition monitoring and fault diagnosis scheme is proposed. The corresponding technique and methods are discussed in detail. And a real application system is developed for the 300MW steam turboset. In this scheme, the system is built on the Internet/Intranet and the Client/Server construction and Web/Server model are adopted. The proposed scheme can guarantee real-time data acquisition and on-line condition analysis simultaneously. And especially, the remote condition monitoring and fault diagnosis can be implemented effectively. The developed system has been installed in a power plant of China. And the plant has obtained great economic benefits from it.