Fault Diagnosis And Pattern Analysis For Rotating Machinery Based On Kernel Methods

Rotating machinery are the key equipments in supporting industry of national economy. It has great economic and social significance to study on the rotating machinery fault diagnosis, for it can ensure a safe, reliable, efficient, long period, full load and quality environment for equipment operation, avoid enormous economic loss and disastrous accident. The process of rotating machinery fault diagnos is a pattern analysis process, and the kernel method represented by Support Vector Machine brought the third revolution of pattern analysis. This method implicit maps original spatial data to feature space through kernel function, seeks for linear relationship in feature space to achieve the efficient solving of nonlinear problem. The rotating machinery faults are usual nonlinear behaviour, and the kernel method is applicable to rotating machinery fault diagnosis and modal analysis especially.Besides SVM, there are some typica kernel methods such as KPCA, KICA, KC, KFD and so on. This paper expanded around the kernel method and its application on rotating machinery fault diagnosis, the main research contents include:1. Acquisition and preprocessing of rotating machinery vibration signal aimed kernel method applicationIt experimented on vibration test of typical rotating machinery faults and collected datas based on Machinery Fault Simulator. Through extracting time domain features, energy, entropy, energy entropy, sequential characteristic and correlation dimension of different frequency bands by wavelet decomposition and empirical mode decomposition, it constituted eleven feature libraries, and provide a data base for applied research of kernel function. According to the deficiency that EMD is easy to produce redundancy IMF, it proposed a improved HHT method. After wavelet packet de-noising, it took correlation coefficients which between each IMF and original signal as the judgment basis, then rejected the redundancy IMF. The fault diagnosis example of rolling bearing shows that this method can extract weak signal fault feature effectively. 2. Optimization of KPCA kernel parameter and research of de-noising method aimed rotating machinery fault diagnosIt revealed the regular patterns of kernel function and kernel parameter on KPCA performance, discovered both gaussian kernel and polynomial kernel, when accumulative ratio above 0.85, the number of kernel principal component decreases with the increase of kernel parameter, and finally takes on convergence status. The KPCA which selected gaussian kernel has a better clustering performance on kernel principal component. It suggests choosing gaussian kernel parameterσ≥25 when conducting KPCA on feature vectors has no prior knowledge.It proposed a signal de-noising method based on KPCA, which overcomed the deficiency of common de-noising methods such as filter and wavelet de-noising need prior knowledge in practical application. It expanded one-dimensional observed signal as the multi-dimension vector through the phase space reconstruction, then extracted kernel principal component by KPCA to achieve signal de-noising, and the whole process needn't any prior knowledge. It verified the effectiveness of this method by simulation and vibration signal de-noising example of rolling bearing.3. Fault signal de-noising and feature extraction of rotating machinery based on KICAIt proposed a signal de-noising method based on KICA, this method expanded one-dimensional observed signal as the multi-dimension vector through introducing the adaptation noise component, then executed KICA to implement noise separation, achieved the goal of de-noising. The de-noising process of KICA won't be influenced by signal-to-noise ratio, and the other de-noising methods can't be compared with it. It verified the effectiveness of this method by signal de-noising of unbalanced rotor.It defined the KIC characteristic quantity of rotating machinery, measured signal analysis showed that KIC can identify the fault of bearing and gear well, and it could be the sensitive characteristic quantity in fault diagnosis.4. Rotating machinery fault diagnosis based on (kernel) clustering It proposed a EMD-fuzzy clustering method and a AR-parameter estimation fuzzy clustering method, and verified the effectiveness of these methods by examples of frolling bearing fault diagnosis and evaluate the degree of performance degradation.It proposed a clustering match method based on the bi-spectrum distribution area, this method can effectively overcomed the deficiency that traditional fault diagnosis methods based on the peak of frequency spectrum are easy interfered by mixing, and verified this method's effectiveness by rolling bearing and gear fault diagnosis examples.It proposed two kinds of kernel clustering algorithms, and key researched the determination method of initial kernel clustering center and further kernel clustering center, verified the effectiveness of this method by examples.5. Rotating machinery fault diagnosis based on SVM and multi-vibration information fusionIt proposed a multi-vibration information fusion rotating machinery fault diagnosis method based on SVM, it can achieve high precision rolling bearing and gear fault diagnosis through SVM fusion of single characteristic quantity from multi sensors.It studied on the rotating machinery fault diagnosis based on base, SVM fused weak signals from base multi-sensors, which effectively achieved the fault diagnosis of rotor crack and rolling bearing. Installing sensor on the base has generality which can overcome some on site questions such as inconvenience of sensor installation, and has a great application prospect.