Intelligent Fault Diagnosis Method Of Rotating Machinery Based On Geometric Model Classification

Rotating machinery chronically serves under extremely harsh working conditions,and thus its operating state will inevitably degrade or even fail.Once the key components in rotating machinery cause faults,such as bearings,gears,rotors,etc.,it will affect the normal operation of the entire mechanical system,resulting in unplanned shutdowns,reduced production efficiency,and even serious production safety accidents.Therefore,the research on rotating machinery fault diagnosis has very important engineering application value for ensuring the service quality of equipment and improving the reliability and safety of the system.With the rapid development of monitoring technology and computer technology,the intelligent diagnosis method of rotating machinery based on machine learning has received extensive attention from relevant scholars at home and abroad,and a series of gratifying research results have been achieved.Among numerous machine learning methods,the classification methods based on geometric models,such as hypersphere,convex hull,and hyperdisk,have obvious advantages in fault diagnosis due to the solid theoretical foundation and outstanding geometric interpretability.However,most of the existing geometric model classification(GMC)methods have the shortcomings of insufficient noise robustness,weak multi-channel information fusion ability,and inability to directly deal with high-order complex data.Therefore,under the support of the National Natural Science Foundation of China(No.51875183,51975193),this dissertation takes rotating machinery and its core components as the research object,conducts in-depth research and exploration on GMC methods,and proposes a variety of improved classification models based on the geometric models of convex hull and hyperdisk,providing new possibilities for further development of intelligent fault diagnosis of rotating machinery.The main research contents of this dissertation are concluded as follows:(1)Geometrically,support matrix machine(SMM)is a matrix-form convex hull classification model,which can directly process matrix data to mine their structural information between rows or columns.However,SMM has poor classification performance when dealing with complex matrix data,such as XOR data.To this end,a non-parallel least squares support matrix machine(NPLSSMM)model is proposed in this dissertation.Different from the existing matrix-form classification models,NPLSSMM constructs a pair of non-parallel classification hyperplanes to classify samples of different classes with the maximum margin,where every hyperplane is required to be as close as possible to the samples of one class while being as far as possible from other samples.This nonparallel classification strategy enables the classification hyperplanes to adaptively adjust their position according to the category distribution of samples,largely enhancing the flexibility and generalization of the proposed model.In addition,the designed matrix-form least-squares constraints significantly improve the computation efficiency of NPLSSMM.The experimental results show that the proposed NPLSSMM model has good fault diagnosis performance of rolling bearings.(2)Aiming at the problems that the existing convex hull classification models have insufficient robustness and weak ability to process unbalanced data,a robustness imbalanced convex hull-based classification(RICHC)model is proposed for intelligent fault diagnosis of bevel gearboxes.First,according to the role of different samples in class distribution estimation,a confidence function is designed for RICHC to reduce the weights of outliers and noisy samples,which will make the boundary of the convex hulls more compact and improve the robustness.At the same time,an adaptive scaling strategy is constructed for RICHC to control the scaling of the convex hulls between different classes,and the scaling ratio is determined by the dynamic imbalance factor between the majority class and the minority class.Based on this strategy,a more accurate classification hyperplane will be obtained for RICHC to improve the unbalanced data processing ability.The effectiveness and applicability of the proposed method is verified on bevel gearbox fault data,and the experimental results show that compared with other models,the proposed method has stronger anti-interference ability against noise and outliers,and the method has more excellent class imbalance classification performance.(3)Aiming at the shortcomings of traditional machine learning methods in classifying symmetric positive definite(SPD)matrices,a Riemannian maximum margin flexible convex hull(RMMFCH)model is proposed.RMMFCH uses the Riemannian metric to evaluate the sample distribution,and constructs the optimal classification hyperplane in the Riemannian geometric space to separate different types of convex hulls under the principle of maximum margin classification.In addition,a fault feature representation method based on statistical-enhanced covariance matrix(SECM)is proposed to fully mine the global-local information of raw data and preserve the interaction between local features.By integrating SECM and RMMFCH,a bearing fault diagnosis scheme based on SECM and RMMFCH is constructed in this paper.The effectiveness and applicability of the proposed scheme are verified by two bearing fault datasets,and the experimental results show that SECM can effectively characterize the sensitive fault information of bearings,and the fault diagnosis performance of RMMFCH is better than traditional fault diagnosis models based on Euclidean metric.(4)Aiming at the problem of information fusion under multi-channel monitoring data,a fault feature representation method of rolling bearing called multi-channel fusion covariance matrix(MFCM)is proposed.In addition,to make full use of the manifold structure information among MFCM,the concept of Riemannian manifold is introduced into the geometric model of hyperdisk,and a maximum margin Riemannian manifold-based hyperdisk(MMRMHD)model is proposed to construct the nonlinear mapping relationship of MFCM and bearing faults,so as to intelligently identify different faults of rolling bearings.The experimental results show that the fault diagnosis method based on MFCM and MMRMHD can effectively achieve multi-channel information fusion,and accurately distinguish different bearing faults with outstanding fault diagnosis performance.(5)A one-class hyperdisk(OCHD)model is proposed by introducing the hyperdisk geometric model into the one-class classification field.By integrating symplectic principal component analysis(SPCA)and OCHD,a fault detection method of rotating machinery is designed in this dissertation.First,SPCA is used to map raw vibration signals into a symplectic space,and then the symplectic eigenvalues(SE)that can best characterize the main energy of the original data are extracted as insensitive eigenvectors.Subsequently,OCHD is constructed with the extracted SE features to achieve the intelligent fault detection of rotating machinery.The experimental results show that the proposed OCHD model can effectively detect the early weak faults of rotating machinery,and has good engineering application prospects.