Intelligent Fault Diagnosis Theory And Its Application On Hoist Machine

Large scale machines are widely used in industry. The safe and reliable operationscan effectively improve the economic and social profits. As a result, the online perfor-mance monitoring of manufacturing process is essential for ensuring process safety andthe delivery of high quality of products. Although the fault detection and diagnosis tech-nologies for machines have been extensively researched in recent years, some openproblems have not yet resolved. The reasons are as following: on the one hand, the largemachines consist of many complex and different components, operate in complex andpoor working conditions and strong noising background. Thus, it is hard to model themin mathematics; On the other hand, a large amount of data collected from machines ischaracterized with complex nonlinearity, nonstationary, strong noises and uncertainties,resulting in more and more difficult to manage, for example, process and understand thesystem structure, identification of process states, feature extraction, fault monitoring sta-tistics construction, decision function development and etc. Inspired form the advancedtheory and technologies of machine learning, fault detection and diagnosis, artificial in-telligence and manifold learning theory et al., Taking account into the above problems,we take the large scale hoist as research subjects and study the theory and applications ofintelligent fault detection and diagnosis in dissertation. The main work includes the fol-lowing:(1) Signal processing method for dynamic and nonlinear signal and the constructionof fault are studied.(1) Hilbert-Huang transform-Data dependent kernel independentcomponent analysis (HHT-DDKICA) method is proposed to denoise and extract featuresfor dynamic and nonlinear vibration signals, i.e. a new denoising algorithm based oncorrelations among neighboring intrinsic mode functions (IMFs) coefficients obtained byHHT is proposed, and the true IMFs describing the original signals are selected to elimi-nate spurious IMFs according to signal energy criteria.(2) DDKICA is presented to de-termine intrinsic information source from IMFs, and a model selection criteria in the em-pirical feature space is also given.(3) Support vector data description (SVDD) is adoptedfor fault monitoring, and new statistics and confidence limits are established. Hoist ma-chinery application shows the efficiencies of the proposed method.(2)The applications of multiscale theory in signals denoise and fault feature genera-tion are researched. The morphological gradient wavelet (MGW) is performed to elimi-nate signal noises. The gear vibration signals are decomposed into multiple scales byMGW, the detailed coefficients in each scale are processed using soft threshold de-nosingand the true fault signals are reconstructed by the processed wavelet coefficients. Mul- ti-resolution S transform is employed to analyzed the reconstructed vibration signals, thegear fault features with good time and frequency resolution can be extracted from thespectral graphs. Simulations show that the proposed method can maintain multiscleproperty, and does not involve the problem of negative frequency and cross frequencydisturbances, resulting to extract the gear fault features effectively.(3)The applications of advanced machine learning algorithms in fault monitoringand disgnosis are researched. Inspired from manifold learning and multiple kernel learn-ing theory, a novel dimensional reduction algorithm called multiple kernel orthogonallocality discriminative analysis with globality preserving (MKOLDAGP) is developed todeal with redundant and heterogeneous features. The local and global geometric structureof the projected data in low dimensional space through MKOLDAGP is consistent withthat of original data set in high dimension space, the extracted feature also include locallydiscriminative information and the distributions of the local clusters approximate Gauss-ian distribution, overcoming the problem that nonlinearity and non-Gaussian of data re-sult in unsatisfying performance of Gaussian mixture model (GMM). A two-stage itera-tive optimization algorithm is proposed to adaptively adjust the weights and parametersof kernel functions, and then the fault statistics of GMM are developed to online condi-tion monitoring machine. Finally, a case study of hoist illustrates the efficiency of theproposed algorithm on the information extraction and fault detection(4)The imbalancedv NSVDD for multiclass classification is developed. To re-duce the influences of noise and outliers and correct the optimization problem in Ref[199],new unbalance datav NSVDD algorithm for multiclass classification is pro-posed. The samples are weighted to deal with the unbalanced data classification problem,and the method to determine the weight coefficients are deduced in theory. The proposedalgorithm can be extended to nonlinear cases by means of kernel trick. Most multiclassclassification methods are lack of rejection decision and the distances between the sam-ple and hypersphere centers in different feature spaces are not equal to their true distanc-es, which influence classification performance and reliability. Combining relative dis-tance with K-NN rule, a multiclass classification method is presented to deal with theabove problems. The results of benchmark testing show that the proposed method canprovide lower classification errors, overcoming the unbalanced data problem.