A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envel...A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method （E-CICA） is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform（DWT） method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio（ER） of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals.展开更多
Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault ...Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.展开更多
The stochastic extended finite-fault simulation method(EXSIM)is a widely used tool in seismological research,with applications in ground motion prediction and simulation,seismic hazard analysis,and engineering studies...The stochastic extended finite-fault simulation method(EXSIM)is a widely used tool in seismological research,with applications in ground motion prediction and simulation,seismic hazard analysis,and engineering studies.However,recent studies have revealed a significant limitation:EXSIM tends to overpredict ground motions in the low-to-intermediate frequency range,particularly for large thrust earthquakes that are often characterized by a double-corner-frequency source model.To address this issue and enhance simulation accuracy,this study introduces two key improvements:(1)a novel asperity-distributed stress-drop composite fault model and(2)a hybrid application of EXSIM with the composite fault model.The proposed method is validated through its application to the 2013 M_(w)6.7 Lushan earthquake that occurred in China and six thrust earthquakes with an M_(w)≥6.5 in Japan.By comparing the simulated ground motions with recorded data,the results demonstrate that the improved method achieves consistent accuracy across the high-and low-frequency spectrum(combined goodness-of-fit:CGOF<0.35).This study significantly broadens the applicability of stochastic finite-fault simulations,enabling more reliable predictions for a wider range of seismic scenarios,including complex thrust faulting events.展开更多
Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has...Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has two limitations:(1)the complex and changeable environmental noise,which cannot ensure the high-performance diagnosis of the model in different noise domains and(2)the possibility of multiple faults occurring simultaneously,which brings challenges to the model diagnosis.This paper presents a novel anti-noise multi-scale convolutional neural network(AM-CNN)for solving the issue of compound fault diagnosis under different intensity noises.First,we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function.Additionally,considering the strong coupling of input information,we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model’s robustness and effectiveness.Finally,a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults.The proposed AM-CNN is verified under our collected compound fault dataset.On average,AM-CNN improves 39.93%accuracy and 25.84%F1-macro under the no-noise working condition and 45.67%accuracy and 27.72%F1-macro under different intensity noise working conditions compared with the existing methods.Furthermore,the experimental results show that AM-CNN can achieve good cross-domain performance with 100%accuracy and 100%F1-macro.Thus,AM-CNN has the potential to be an accurate and stable fault diagnosis tool.展开更多
It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under ...It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under non-stationary operation turns to be a challenging task.Multiwavelet with two or more base functions may match two or more features of compound faults,which may supply a possible solution to compound faults detection.However,the fixed basis functions of multiwavelet transform,which are not related with the vibration signal,may reduce the accuracy of compound faults detection.Moreover,the decomposition results of multiwavelet transform not being own time-invariant is harmful to extract the features of periodical impulses.Furthermore,multiwavelet transform only focuses on the multi-resolution analysis in the low frequency band,and may leave out the useful features of compound faults.To overcome these shortcomings,a novel method called adaptive redundant multiwavelet packet(ARMP) is proposed based on the two-scale similarity transforms.Besides,the relative energy ratio at the characteristic frequency of the concerned component is computed to select the sensitive frequency bands of multiwavelet packet coefficients.The proposed method was used to analyze the compound faults of rolling element bearing.The results showed that the proposed method could enhance the ability of compound faults detection of rotating machinery.展开更多
Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of col...Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault diagnosis.ICF can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault classification.ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience.Simulated and experimental signals of bearing fault are used to validate the performance of ICF.The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.展开更多
The dissociation of a [1-01] superdislocation in Ni 3Al was studied by computer simulation techniques using the embedded atom method (EAM). Three types of dissociation were obtained, depending on the initial position ...The dissociation of a [1-01] superdislocation in Ni 3Al was studied by computer simulation techniques using the embedded atom method (EAM). Three types of dissociation were obtained, depending on the initial position of elastic centers of the superdislocation. One is the stable planar dissociation that the superdislocation dissociates on only one {111} plane into a pair of 1/2[1-01] superpartials separated by antiphase boundary (APB). Another stable dissociation is that it occurs on two adjacent {111} planes joined by an intersecting {111} or (010) plane. The metastable one is that the dissociation occurs in T shape: the superdislocation dissociates on two intersecting {111} planes into three partials: one 1/2[1-01] partial and two widely separated 1/6〈112〉 Shockley partials with a complex stacking fault (CSF) in between.展开更多
Owing to the excellent elastic properties and chemical stability,binary metal or light element borides,carbides and nitrides have been extensively applied as hard and low-compressible materials.Researchers are searchi...Owing to the excellent elastic properties and chemical stability,binary metal or light element borides,carbides and nitrides have been extensively applied as hard and low-compressible materials.Researchers are searching for harder materials all the time.Recently,the successful fabrication of nano-twinned cubic BN(Tian et al.Nature 493:385–388,2013)and diamond(Huang et al.Nature 510:250–253,2014)exhibiting superior properties than their twin-free counterparts allows an efficient way to be harder.From this point of view,the borides,carbides and nitrides may be stronger by introducing twins,whose formation tendency can be measured using stacking fault energies(SFEs).The lower the SFEs,the easier the formation of twins.In the present study,by means of first-principles calculations,we first calculated the fundamental elastic constants of forty-two borides,seventeen carbides and thirty-one nitrides,and their moduli,elastic anisotropy factors and bonding characters were accordingly derived.Then,the SFEs of the{111}<112>glide system of twenty-seven compounds with the space group F43 m or Fm3m were calculated.Based on the obtained elastic properties and SFEs,we find that(1)light element compounds usually exhibit superior elastic properties over the metal borides,carbides or nitrides;(2)the 5 d transitionmetal compounds(ReB2,WB,OsC,RuC,WC,OsN2,TaN and WN)possess comparable bulk modulus(B)with that of cBN(B=363 GPa);(3)twins may form in ZrB,HfN,PtN,VN and ZrN,since their SFEs are lower or slightly higher than that of diamond(SFE=277 mJ/m^2).Our work can be used as a valuable database to compare these compounds.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51475034)
文摘A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to realize single channel compound fault diagnosis of bearings and improve the diagnosis accuracy, an improved CICA algorithm named constrained independent component analysis based on the energy method （E-CICA） is proposed. With the approach, the single channel vibration signal is firstly decomposed into several wavelet coefficients by discrete wavelet transform（DWT） method for the purpose of obtaining multichannel signals. Then the envelope signals of the reconstructed wavelet coefficients are selected as the input of E-CICA algorithm, which fulfills the requirements that the number of sensors is greater than or equal to that of the source signals and makes it more suitable to be processed by CICA strategy. The frequency energy ratio（ER） of each wavelet reconstructed signal to the total energy of the given synchronous signal is calculated, and then the synchronous signal with maximum ER value is set as the reference signal accordingly. By this way, the reference signal contains a priori knowledge of fault source signal and the influence on fault signal extraction accuracy which is caused by the initial phase angle and the duty ratio of the reference signal in the traditional CICA algorithm is avoided. Experimental results show that E-CICA algorithm can effectively separate out the outer-race defect and the rollers defect from the single channel compound fault and fulfill the needs of compound fault diagnosis of rolling bearings, and the running time is 0.12% of that of the traditional CICA algorithm and the extraction accuracy is 1.4 times of that of CICA as well. The proposed research provides a new method to separate single channel compound fault signals.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 52205100,52275111,and 52205101in part by the Natural Science Foundations of Guangdong Province-China under Grants 2023A1515012856in part by China Postdoctoral Science Foundation under Grant 2022M711197.
文摘Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.
基金National Key Research and Development Program of China under Grant No.2022YFC3003601National Natural Science Foundation of China under Grant No.52478570+1 种基金Heilongjiang Provincial Natural Science Foundation Outstanding Youth Program under Grant No.J020245002the Key Research and Development Program of Xinjiang Production and Construction Corps under Grant No.2024AB077。
文摘The stochastic extended finite-fault simulation method(EXSIM)is a widely used tool in seismological research,with applications in ground motion prediction and simulation,seismic hazard analysis,and engineering studies.However,recent studies have revealed a significant limitation:EXSIM tends to overpredict ground motions in the low-to-intermediate frequency range,particularly for large thrust earthquakes that are often characterized by a double-corner-frequency source model.To address this issue and enhance simulation accuracy,this study introduces two key improvements:(1)a novel asperity-distributed stress-drop composite fault model and(2)a hybrid application of EXSIM with the composite fault model.The proposed method is validated through its application to the 2013 M_(w)6.7 Lushan earthquake that occurred in China and six thrust earthquakes with an M_(w)≥6.5 in Japan.By comparing the simulated ground motions with recorded data,the results demonstrate that the improved method achieves consistent accuracy across the high-and low-frequency spectrum(combined goodness-of-fit:CGOF<0.35).This study significantly broadens the applicability of stochastic finite-fault simulations,enabling more reliable predictions for a wider range of seismic scenarios,including complex thrust faulting events.
基金supported by the National Key R&D Program of China(Grant No.2020YFB1709604)the State Key Laboratory of Mechanical System and Vibration(Grant No.MSVZD202103)+1 种基金the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102)。
文摘Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has two limitations:(1)the complex and changeable environmental noise,which cannot ensure the high-performance diagnosis of the model in different noise domains and(2)the possibility of multiple faults occurring simultaneously,which brings challenges to the model diagnosis.This paper presents a novel anti-noise multi-scale convolutional neural network(AM-CNN)for solving the issue of compound fault diagnosis under different intensity noises.First,we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function.Additionally,considering the strong coupling of input information,we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model’s robustness and effectiveness.Finally,a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults.The proposed AM-CNN is verified under our collected compound fault dataset.On average,AM-CNN improves 39.93%accuracy and 25.84%F1-macro under the no-noise working condition and 45.67%accuracy and 27.72%F1-macro under different intensity noise working conditions compared with the existing methods.Furthermore,the experimental results show that AM-CNN can achieve good cross-domain performance with 100%accuracy and 100%F1-macro.Thus,AM-CNN has the potential to be an accurate and stable fault diagnosis tool.
基金supported by the National Natural Science Foundation of China (Grant Nos. 50975220 and 51035007)the National Basic Research Program of China ("973" Program) (Grant No. 2009CB724405)the Important National Science and Technology Specific Projects (Grant No.2010ZX04014-016)
文摘It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under non-stationary operation turns to be a challenging task.Multiwavelet with two or more base functions may match two or more features of compound faults,which may supply a possible solution to compound faults detection.However,the fixed basis functions of multiwavelet transform,which are not related with the vibration signal,may reduce the accuracy of compound faults detection.Moreover,the decomposition results of multiwavelet transform not being own time-invariant is harmful to extract the features of periodical impulses.Furthermore,multiwavelet transform only focuses on the multi-resolution analysis in the low frequency band,and may leave out the useful features of compound faults.To overcome these shortcomings,a novel method called adaptive redundant multiwavelet packet(ARMP) is proposed based on the two-scale similarity transforms.Besides,the relative energy ratio at the characteristic frequency of the concerned component is computed to select the sensitive frequency bands of multiwavelet packet coefficients.The proposed method was used to analyze the compound faults of rolling element bearing.The results showed that the proposed method could enhance the ability of compound faults detection of rotating machinery.
基金supported by the Major National Science and Technology Projects(No.2017-IV-0008-0045)the National Natural Science Foundation of China(Nos.51675262 and 51975276)+1 种基金the Advance Research Field Fund Project of China(No.61400040304)the National Key Research and Development Program of China(No.2018YFB2003300)。
文摘Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault diagnosis.ICF can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault classification.ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience.Simulated and experimental signals of bearing fault are used to validate the performance of ICF.The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.
文摘The dissociation of a [1-01] superdislocation in Ni 3Al was studied by computer simulation techniques using the embedded atom method (EAM). Three types of dissociation were obtained, depending on the initial position of elastic centers of the superdislocation. One is the stable planar dissociation that the superdislocation dissociates on only one {111} plane into a pair of 1/2[1-01] superpartials separated by antiphase boundary (APB). Another stable dissociation is that it occurs on two adjacent {111} planes joined by an intersecting {111} or (010) plane. The metastable one is that the dissociation occurs in T shape: the superdislocation dissociates on two intersecting {111} planes into three partials: one 1/2[1-01] partial and two widely separated 1/6〈112〉 Shockley partials with a complex stacking fault (CSF) in between.
基金supported by the National Natural Science Foundation of China (Nos. 11427806, 51471067, 51671082, 51671086 and 51302313)the National Key Research and Development Program of China (No. 2016YFB0300801)
文摘Owing to the excellent elastic properties and chemical stability,binary metal or light element borides,carbides and nitrides have been extensively applied as hard and low-compressible materials.Researchers are searching for harder materials all the time.Recently,the successful fabrication of nano-twinned cubic BN(Tian et al.Nature 493:385–388,2013)and diamond(Huang et al.Nature 510:250–253,2014)exhibiting superior properties than their twin-free counterparts allows an efficient way to be harder.From this point of view,the borides,carbides and nitrides may be stronger by introducing twins,whose formation tendency can be measured using stacking fault energies(SFEs).The lower the SFEs,the easier the formation of twins.In the present study,by means of first-principles calculations,we first calculated the fundamental elastic constants of forty-two borides,seventeen carbides and thirty-one nitrides,and their moduli,elastic anisotropy factors and bonding characters were accordingly derived.Then,the SFEs of the{111}<112>glide system of twenty-seven compounds with the space group F43 m or Fm3m were calculated.Based on the obtained elastic properties and SFEs,we find that(1)light element compounds usually exhibit superior elastic properties over the metal borides,carbides or nitrides;(2)the 5 d transitionmetal compounds(ReB2,WB,OsC,RuC,WC,OsN2,TaN and WN)possess comparable bulk modulus(B)with that of cBN(B=363 GPa);(3)twins may form in ZrB,HfN,PtN,VN and ZrN,since their SFEs are lower or slightly higher than that of diamond(SFE=277 mJ/m^2).Our work can be used as a valuable database to compare these compounds.
