To solve the problems of low precision of weak feature extraction,heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage(UHV)equipment key parts,an au...To solve the problems of low precision of weak feature extraction,heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage(UHV)equipment key parts,an automatic feature extraction algorithm is proposed.Firstly,the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method.Then,binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation.Finally,the automatic identification of weld defect area is realized based on the sequential traversal of binary tree.Several characteristic analysis dimensions are established for weld defects of UHV key parts,including defect area,perimeter,slenderness ratio,duty cycle,etc.The experiment using theweld detection image of the actual production site shows that the proposedmethod can effectively extract theweak feature information ofweld defects and further provide reference for decision-making.展开更多
This paper discusses the modal features of weakly-viscoelastic material structures both for single-modulus and multi-modulus materials. It is the eigenvalues of these structures that are the roots of a series of ratio...This paper discusses the modal features of weakly-viscoelastic material structures both for single-modulus and multi-modulus materials. It is the eigenvalues of these structures that are the roots of a series of rational fraction polynomial equations. A theorem about the roots of these equations is proved in the paper. Based on it, some important conclusions about the modal features of the weakly viscoelastic material structures are given according to their dynamic behaviors.展开更多
Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine.Its structural and operational characteristics make it prone to frequent and highly hazardous faults,which can easily lea...Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine.Its structural and operational characteristics make it prone to frequent and highly hazardous faults,which can easily lead to catastrophic accidents such as the failure of the entire rotor system.Therefore,it is significant to realize vibration fault monitoring and warning of inter-shaft bearings.Aero-engine vibration has complex characteristics,with diagnostic methods facing major limitations.Firstly,the compact engine structure and inter-shaft bearing placement cause weak,attenuated fault signals with significant interference,complicating feature extraction.Secondly,in counter-rotating dual-rotor systems,inter-shaft bearing components rotate oppositely,producing higher characteristic frequencies than co-rotating bearings.High-frequency fault signals often overlap or appear harmonic,and their propagation is easily affected by structural complexities,making accurate monitoring challenging.To address these challenges,a method combining Traversal Index Enhanced-gram(TIEgram)and autocorrelation(AC)is proposed for extracting weak fault features in inter-shaft bearings.TIEgram selects the optimal frequency band for resonance demodulation,isolating fault-related signal components.To counter nonstationary signals from aero-engine dynamics,slip-ratio domain order tracking transforms time-domain signals into angular-domain stationary signals.Autocorrelation analysis then yields the squared envelope autocorrelation spectrum,compared with bearing fault characteristic orders for diagnosis.Simulation and experimental results demonstrate the method's effectiveness in extracting weak inter-shaft bearing fault features.展开更多
文摘To solve the problems of low precision of weak feature extraction,heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage(UHV)equipment key parts,an automatic feature extraction algorithm is proposed.Firstly,the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method.Then,binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation.Finally,the automatic identification of weld defect area is realized based on the sequential traversal of binary tree.Several characteristic analysis dimensions are established for weld defects of UHV key parts,including defect area,perimeter,slenderness ratio,duty cycle,etc.The experiment using theweld detection image of the actual production site shows that the proposedmethod can effectively extract theweak feature information ofweld defects and further provide reference for decision-making.
文摘This paper discusses the modal features of weakly-viscoelastic material structures both for single-modulus and multi-modulus materials. It is the eigenvalues of these structures that are the roots of a series of rational fraction polynomial equations. A theorem about the roots of these equations is proved in the paper. Based on it, some important conclusions about the modal features of the weakly viscoelastic material structures are given according to their dynamic behaviors.
基金Supported by the Joint Fund of the Ministry of Education of China(Grant No.8091B022203)Youth Talent Support Project(Grant No.2022-JCJQ-QT-059).
文摘Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine.Its structural and operational characteristics make it prone to frequent and highly hazardous faults,which can easily lead to catastrophic accidents such as the failure of the entire rotor system.Therefore,it is significant to realize vibration fault monitoring and warning of inter-shaft bearings.Aero-engine vibration has complex characteristics,with diagnostic methods facing major limitations.Firstly,the compact engine structure and inter-shaft bearing placement cause weak,attenuated fault signals with significant interference,complicating feature extraction.Secondly,in counter-rotating dual-rotor systems,inter-shaft bearing components rotate oppositely,producing higher characteristic frequencies than co-rotating bearings.High-frequency fault signals often overlap or appear harmonic,and their propagation is easily affected by structural complexities,making accurate monitoring challenging.To address these challenges,a method combining Traversal Index Enhanced-gram(TIEgram)and autocorrelation(AC)is proposed for extracting weak fault features in inter-shaft bearings.TIEgram selects the optimal frequency band for resonance demodulation,isolating fault-related signal components.To counter nonstationary signals from aero-engine dynamics,slip-ratio domain order tracking transforms time-domain signals into angular-domain stationary signals.Autocorrelation analysis then yields the squared envelope autocorrelation spectrum,compared with bearing fault characteristic orders for diagnosis.Simulation and experimental results demonstrate the method's effectiveness in extracting weak inter-shaft bearing fault features.
