Study On Intelligent Diagnosis Technology Of Rolling Bearing Based On Modern Signal Analysis And Neural Networks

Rolling bearing is a important part of all kinds of mechanical and electrical equipments. The longevity can not be predicted for it's great randomicity. The working state of rolling bearing determines whether the equipment can be used normally. Therefore , mastery of the working state and the form and development of the fault of rolling bearing is one of the important research in the advanced field of mechanical fault diagnosis. The random vibration signals of bearings and components , which are employed in monitoring and diagnosing their working state is the common used method in the study of mechanical fault monitoring and diagnosis. Monitoring and diagnosing of mechanical working state that based on the parameter of vibration signals, the signals captured from fault bearings are composed of not only itself but also other parts. The typical unstable, nonlinear and non-Gauss signals are so faint that usually covered with vibration signals generated by other parts and quantities of random signals. How to capture the fault features from faint signals of fault bearing efficiently and recognize working state correctly are the two linchpins of solving the problem in the field of engineering.In this dissertation, the working state and fault features of rolling bearing which is based on mathematic method and means of modern signal analysis such as time-frequency analysis, time-sequence analysis, principal components analysis, higher-order spectrum analysis, wavelet analysis, fractal theory and neural networks is deeply studied. Some new solution is presented. The main research achievement and conclusion are as follows: 1. Vibration signal spectrum of rolling bearing is analysed detailedly with traditional and modern spectrum technology. The experimental result shows that the initial fault signal is very faint. As a result, spectrum analyzing from capturing signals and thus obtaining characteristic frequency of rolling bearing is in vain, whichever spectrum analyzing method is used.2. Vibration signal of rolling bearing which is based on envelopment analysis methods is studied. The experiment results verify that it is difficult to explain the envelopment chart reasonably if the rolling bearing possesses more than one damage point. Actually , seldom does inner and outer rings, roller or retainer appear single damage when attains its longevity or damaged by other reasons. The properties of damage are some fault type of the same kind and non-equal distances distribution. Although envelopment analysis is available in diagnosing fault features of frequency , it should be used with other efficient methods to form a multi-information integrated recognizing system,otherwise,the accuracy rate would be too low.3. STFT and WVD theory and their application are discussed detailedly. Furthermore, simulation and analysis software are presented. Normal and abnormal signals are classified by time-frequency spectrum that in the light of Wigner-Ville distribution. It shows clearly that a new method of reference graphic database which established by time-frequency spectrum is feasible in diagnosing fault of...