Research On Mechanical Fault Diagnosis Methods Based On Signal Local Feature Extraction

The higher and higher demands for operational reliability and safety of the modern production system is the reason for generation and rapid development of the mechanical fault diagnosis technology. With the development of the mechanical equipment towards high speed, heavy load, large scale and complication, traditional fault diagnosis technology can't meet the requirement for equipment diagnosis. Development of signal analysis and artificial intelligence technology supplies powerful tools for improving the fault diagnosis level. Focusing on time-varying and non-stationary feature of vibration signal when faults occur, based on projects of vehicle gear-box testing system, vehicle power transfer testing system, vehicle gear-box gear testing system, and on the research subjects of gear and bearing, research was carried out around the two core problems of fault diagnosis, feature extraction and intelligent diagnosis method. Research keys were the analysis method based on signal local characteristics and intelligent diagnosis model and its application based on artificial immune system (AIS). The main research works are as follows:(1) The main fault types, vibration generating principle and signal feature of the research subjects were introduced, and influences of practical measure and transmitting path on vibration signal were analyzed, which is the basis for latter chapters.(2) Focusing on practical data interfered by noises and background signal, the denoising and feature extraction methods based on morphological wavelet were researched. Two morphological wavelets, minimax-lifting morphological wavelet (MLMW) and multi-element morphological undecimated wavelet decomposition (MMUWD), were constructed based on the general frame of morphological wavelet, then they were used to extract shocking signal's feature, show stronger feature extraction capability than that of traditional wavelet and demodulation analysis. Using MLMW to the CWT grey moment analysis of gear and bearing, results show that MLMW greatly improves the describing capability of CWT grey moment for signal's local time-frequency energy distribution characteristics.(3) Aiming at general analysis methods' basis being not adaptive for signal's local feature, the feature extraction method based on local-wave was researched. A method improving the feature extraction capability of local-wave was proposed, in which noise components and pseudo-components were removed by calculating local-wave mutual information, and morphological wavelet analysis was combined to constrain mode mixing and pseudo-components. With which the decomposition quality and feature extraction capability are improved. Then the information entropy feature analysis method based on local-wave was proposed, which was used to describe signal's complexity in the intrinsic mode space. Simulation and practical data prove the proposed methods' effectiveness.(4) Focusing on features' different importance, correlation and redundancy, and combining local-wave analysis, methods of obtaining feature subset preferable for classification and improving classification ability were introduced from feature extraction and feature selection, and the models of KPCA(kernel principal component analysis) - LSSVM (least squares support vector machines) and BEF(Bayesian evidence framework)- SBS (sequential backward selection)- LSSVM were proposed correspondingly. The former mapped data to high dimension feature space through KPCA, and extracted features on the basis of constraining redundancy and noises. While the latter adopted heuristic searching strategy, and realized adaptive multi-feature subsets selection and optimization. The practical data proves the proposed methods' effectiveness in improving learning and generalization performance of LSSVM.(5) In nature, AIS stimulates biological fault diagnosis mechanism for itself, and has good interpretation. Based on immune identification and immune net metaphor mechanism, the fault diagnosis models based on V-detector algorithm and RS-ABNet fault diagnosis model were proposed. The former is suitable for processing low dimension space problem, while the latter has advantage in processing high dimension space problem. The fault diagnosis of gear and bearing prove the practicability and effectiveness of the above methods, and it will supply a new intelligent method for mechanical fault diagnosis.