Complex Mechanical Data-based Modeling And Fault Diagnosis

Operation vibration signals of mechanical equipment health monitoring are usually the important source of data for fault diagnosis. In order to diagnose faults via these data, linear or nonlinear model based on signal can be used. It can be used to extract fault features directly from the vibration signals too. The effective diagnostic strategy is determined in accordance with results of inspection for nature of the data. If the data comes from the obvious linear system, the diagnosis methods based on linear model are appropriate. If the data are non-linear, the diagnosis method based on nonlinear model can achieve good results. If the data show that mechanical equipment is into the chaotic vibration state, the chaotic features are extracted for fault diagnosis. Therefore, this paper studies some test method of nonlinear data, such as bispectrum analysis that is used to test the characteristics of gear vibration data. Lyapunov index is used to describe chaos quantificationally.Propose the concept of geometry - physical space. All data of the large system are divided into smaller regional data sets, and get the physical partition.Under the rules in the cluster analysis, the data set that expands with time is classed under distance-based classification.The high dimensional data space is reduced into low-dimensional data space using PCA , the original information content is almost unchanged , redundant data is dislodged , the small amount of low dimension data are used to represent the original data, and simulation examples are given.Linear system models and neural network models that applie to nonlinear systems are discussed. The recognition capability of neural network is optimized by considering topological structure optimization, item count of output data, delay step, item count of hidden layer neuron, activation function, etc. It is that identification accuracy and speed are improved. Thus Real time property of identifying neural network models is improved.The concept of model determinacy is provided. Spectrum characteristics of the identified system that is target feature, boxplots of weight matrix row data are analyzed. Spectrum signature presents best determinacy, when there are less outliers of weight values.In this paper a Fault diagnosis method based on virtual response spectrum sequences is proposed. The parallel simulation is performed based on identified accurate models to obtain response sequences correspond to different amplitudes of virtual sine or impulse excition. The series of sine response spectrum and pulse response spectrum can be used to analyze dynamic characteristics of system. The series of response spectra can be used for fault diagnosis of linear or nonlinear systems too, thereby increasing the reliability of fault diagnosis. This method has been applied successfully to the diagnosis of engineering structures.