Study On Immune Mechanism And Support Vector Machine-Based Composite Fault Diagnosis Theory And Experiments

The fault detection and diagnosis technique is an effective way to enhance system reliability and maintainability. In recent years, the intelligent fault diagnosis theory and technology develops very fast. To effectively acquire, transfer, deal with, regenerate and utilize diagnostic information has been the core of intelligent fault diagnosis so that it could exhibit an ability to precisely identify the patterns of faults and predict future faults. Recently, the dominating difficulties that the field of intelligent fault diagnosis faces are terrible lack of typical fault samples and finding problem of diagnosis knowledge, both of which badly prohibit the development of intelligent fault diagnosis theory and technology.Artificial immune system, which has powerful information processing ability, simulates biology immune system. The main function of biology immune system provided with the ability of recognizing"Self"and"Non-self"is to detect, damage and kill the antigen from inside or outside body in the real time. Thus, without the transcendent knowledge of abnormal mode, the algorithm of Negative Selection (NS) derived from that can recognize the normal (Self) or abnormal (Non-self) mode of the detected objects efficiently. Therefore, this algorithm provides the new idea and method to intelligent fault diagnosis.Owing to naissance of Support Vector Machine (SVM), the shortages of learning aspect are solved in many learning algorithms using finite sample data, nonlinear data, and high dimension data. And SVM also overcomes shortcomings of the neural network learning methods, for example, determining the network structure difficultly, slow convergence, local minimum value, over-fitting & under-fitting as well as excessive need for training sample. SVM makes the generalization ability of classifier better in the finite sample condition, which has strong pertinence and applicability for the system fault diagnosis. So the composite intelligent fault diagnosis method is put forward based on support vector machine and artificial immune mechanism. The validity of the founded method is verified through the experiment research of diagnosing fault of the hydraulic pump, the important components in hydraulic systems. These researches have important theoretical significance and engineering practical application value for enriching the fault diagnosis theory.The main works in this dissertation are showed as follows:(1)The origin and development of artificial immune system is summarized and the mechanism and algorithm of artificial immune system is researched. The mechanism of NS algorithm and its realization is studied. The background, theory, characteristics and applications of support vector machine which are advanced study machine presently are expounded. The key problem is studied in applying support vector machine to fault diagnosis, and the foundational realizing steps are brought out for support vector machine applying to fault diagnosis. The application software of SVM is programmed and the choice principle of some kernel function parameters is researched.(2)A composite intelligent fault diagnosis method is brought forward using the support vector machine and the real-valued negative selection algorithm. This composite method is used to the multi-pattern fault diagnosis and recognition of swashplate axial piston pump. The simulation research is shown that the right rate of fault diagnosis is enhanced effectively using this method.(3)Aiming at the modulation phenomenon of the vibration signal, the principle of envelop demodulation based on the complex analytical wavelet cluster is researched. When the equipment generates fault, signal energy distributing each frequency band has a greater change. The wavelet packet decomposition sub-frequency bands characteristics energy method of the envelope signal is introduced for the extraction of signal eigenvectors which can reflect the status of equipment operation.(4)In allusion to some shortage of principal component analysis(PCA) in fault feature selection, a kind of effective nonlinear feature selection method, which based on kernel principal component analysis(KPCA), is suggested and realized. The basic principle and algorithm of KPCA is expatiated. Through the selection of feature vectors, dimensions of feature data and calculating complexity of classifier are decreased effectively, and experiments show that KPCA is sensitive to nonlinear features, and it is suitable for the selection of nonlinear features of hydraulic system faults. (5)Based on the traditional oil-film theory, the slipper/swashplate impact mechanism during the slipper operation is analyzed and the correctness of the theoretical analysis is verified through the experiment. The experiment system based on virtual instrument is composed and the end-cover vibration signal and the outlet pressure signal of pump are selected as the monitored signals. The loose slipper failure and the wearing valve plate are simulated artificially by replacing the normal components of pump with loose slipper piston and worn valve plate. And a mass of data samples of multi-fault pattern acquired. Because vibration signal generated from slipper/swashplate impact is modulated by high frequency resonance signal, the modulation signal has to be demodulated using the wavelet cluster envelope demodulation method, and the envelope signal contains abundant fault information. After the envelope signal is decomposed using wavelet packet, signal eigenvectors which compose the sample set (normal and fault), are extracted and the dimensions of sample set are decreased using KPCA. The samples are diagnosed using composite fault diagnosis method, whose validity is verified.