Time Series Data Mining And Its Applications In Fault Diagnosis

Data Mining is a complex processing. The properties of time series are high dimension and dynamic. Hence, how to mine time series data effectively is an important research subject. Firstly, this dissertation researches the preprocessing work of Time Series Data Mining (TSDM), which includes nonlinearity test, denoise, segmentation. Then, some novel TSDM techniques are introduced into mechanical fault diagnosis field. Finally, this dissertation verifies these TSDM methods and theories via the simulation data and practical examples. The main works can be summed up as following:1) Time series data nonlinearity test researchA nonlinearity test method is proposed, which is integrates a Stochastic Iterative Amplitude Adjusted Fourier Transform (SIAFFT) algorithm and KS test Statistic. Not only weak nonlinear signal and strong nonlinear signal but signal with additive noise, the presented method can attain accurate test results. Moreover, through employing traditional test methods, both results show that this method is superior to the traditional ones in stronger robustness to signal with additive noise and higher sensitive to nonlinear signal.2) The globe projective algorithm and its application in noise reduction and the fault character extraction.A globe projective algorithm is introduced. Its calculation speed and denoise effect is superior to local projective. Based on these advantages, this algorithm is successful applied in purifying the exhaust fan's rotor axis orbit in certain sinter plant. Moreover, according to the fault characteristic of low-speed rolling bearing, this paper proposes a new method in bearing fault diagnosis via integrating globe projective algorithm and resonance demodulation technique. By the proposed method, the rolling bearing fault of a converter trunnion was detected. The diagnosed result is consistent with the fact.3) Time series data segmentation method based on Gath-Geva clusterThis dissertation introduces an online data segmentation method based on Gath-Geva cluster. This algorithm can merge clusters by itself. The most advantage is that auto-searching the optimal segmentation subset without the expert's supports. Hence, it is an important online data segmentation technique in engineering field.4) The time series data classification mining system based on KS testThis dissertation presents a novel data classification mining method based on KS test. By applying it in simulation test and gear fault diagnosis, this method can diagnosis faults though the data samples hold some noise. After using a few bearing time-domain fault data samples to building multi-class KS test classifier system, it classified kinds of faults precisely in a short time. The result shows this method has good classification ability and efficiency. It can satisfy the requirement of intelligent diagnosis.5) The modified SVM and its application in time series predictionThe dissertation modifies the traditional SVM. Through applying evolution strategies algorithm, the optimal penalty factor C,ε-insensitive cost function and Gauss kernel parameterσ. The forecasting result of Lorenz signal verifies the prediction accuracy of modified SVM (ES-SVM) method is higher than the traditional SVM. In addition, the measured practical signal often mixes noise. Hence, the global projective method can be used to reduce noise in advance. Then, applying the proposed method, the better forecasting result will be gained.6) Constructing the remoted monitor and diagnosis system based on TSDMAs a main part of TSDM application, this dissertation discusses data warehouse structure and data type of mechanical online monitor system. Then, the TSDM processing model is built. Based on it, according to the acquirement of a certain sinter plant, the remoted monitor and diagnosis system based on TSDM is constructed.