A fault processing policy was proposed for the circumstance under which all the dual-redundant closed-loop feedback control sensors went wrong.This policy was based on dual-redundant control law and may maintain the p...A fault processing policy was proposed for the circumstance under which all the dual-redundant closed-loop feedback control sensors went wrong.This policy was based on dual-redundant control law and may maintain the performance of turbofan engines.When pair of controlling sensors went wrong and the primary control law was unable to implement,control system performs the backup control law instead of the primary one so that the fault sensors were isolated from the closed control loop.This fault processing policy may not only avoid increasing engine weight and cost with the additional sensors hardware,but also avoid the error of analytical sensor signal.It can improve the engine mission reliability and control quality.展开更多
Recent studies, focused on dihedral angles and intersection processes, have increased understandings of conjugate fault mechanisms. We present new 3-D seismic data and microstructural core analysis in a case study of ...Recent studies, focused on dihedral angles and intersection processes, have increased understandings of conjugate fault mechanisms. We present new 3-D seismic data and microstructural core analysis in a case study of a large conjugate strike-slip fault system from the intracratonic Tarim Basin, NW China. Within our study area, ＂X＂ type NE and NW trending faults occur within Cambrian- Ordovician carbonates. The dihedral angles of these conjugate faults have narrow ranges, 19~ to 62~ in the Cambrian and 26~ to 51~ in the Ordovician, and their modes are 42~ and 44~ respectively. These data are significantly different from the ~60~ predicted by the Coulomb fracture criterion. It is concluded that： （1） The dihedral angles of the conjugate faults were not controlled by confining pressure, which was low and associated with shallow burial; （2） As dihedral angles were not controlled by pressure they can be used to determine the shortening direction during faulting; （3） Sequential slip may have played an important role in forming conjugate fault intersections; （4） The conjugate fault system of the Tarim basin initiated as rhombic joints; these subsequently developed into sequentially active ＂X＂ type conjugate faults; followed by preferential development of the NW-trending faults; then reactivation of the NE trending faults. This intact rhombic conjugate fault system presents new insights into mechanisms of dihedral angle development, with particular relevance to intracratonic basins.展开更多
This paper presents a fault diagnosis method for process faults and sensor faults in a class of nonlinear uncertain systems.The fault detection and isolation architecture consists of a fault detection estimator and a ...This paper presents a fault diagnosis method for process faults and sensor faults in a class of nonlinear uncertain systems.The fault detection and isolation architecture consists of a fault detection estimator and a bank of adaptive isolation estimators,each corresponding to a particular fault type.Adaptive thresholds for fault detection and isolation are presented.Fault detectability conditions characterizing the class of process faults and sensor faults that are detectable by the presented method are derived.A simulation example of robotic arm is used to illustrate the effectiveness of the fault diagnosis method.展开更多
Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and co...Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.展开更多
Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of productio...Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.展开更多
<Abstract>A novel fault detection and diagnosis method was proposed, using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur. It aims at parameter failures, ...<Abstract>A novel fault detection and diagnosis method was proposed, using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur. It aims at parameter failures, and the parameters are updated via on-line correction. As it can predict the trend of process and determine the existence of malfunctions simultaneously, this method does not need to design problem-specific observer to estimate unmeasured state variables. Application of the proposed method is presented on one water tank and one aromatization reactor, and the results are compared with those from the traditional method.展开更多
Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode che...Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis(LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis(PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.展开更多
Abstract The Nansha ultra-crust layer-block is confined by ultra-crustal boundary faults of distinctive features, bordering the Kangtai-Shuangzi-Xiongnan extensional faulted zone on the north, the Baxian-Baram-Yoca-Cu...Abstract The Nansha ultra-crust layer-block is confined by ultra-crustal boundary faults of distinctive features, bordering the Kangtai-Shuangzi-Xiongnan extensional faulted zone on the north, the Baxian-Baram-Yoca-Cuyo nappe faulted zone on the south, the Wan'an-Natuna strike-slip tensional faulted zone on the west and the Mondoro-Panay strike-slip compressive faulted zone on the east. These faults take the top of the Nansha asthenosphere as their common detachmental surface. The Cenozoic dynamic process of the ultra-crust layer-block can be divided into four stages: K2-E21, during which the northern boundary faults extended, this ultra-crust layer-block was separated from the South China-Indosinian continental margin, the Palaeo-South China Sea subducted southwards and the Sibu accretion wedge was formed; E22-E31, during which the Southwest sub-sea basin extended and orogeny was active due to the collision of the Sibu accretion wedge; E32-N11, during which the central sub-sea basin extended, the Miri accretion wedge was formed and “A-type” subduction of the southern margin of the north Balawan occurred; N12-the present, during which large-scale thrusting and napping of the boundary faults in the south and mountain-building have taken place and the South China Sea stopped its extension.展开更多
A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively rem...A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.展开更多
Leaching process is the first step in zinc hydrometallurgy, which involves the complex chemical reactions for dissolving zinc bearing material in dilute sulfuric acid. Ensuring the safe running of the process is a key...Leaching process is the first step in zinc hydrometallurgy, which involves the complex chemical reactions for dissolving zinc bearing material in dilute sulfuric acid. Ensuring the safe running of the process is a key point in the operation. An expert fault diagnosis system for the leaching process was proposed, which has been implemented in a nonferrous metals smeltery. The system architecture and the diagnosis procedure were presented, and the rule models with the certainty factor were constructed based on the empirical knowledge, empirical data and statistical results on past fault countermeasures, and an expert reasoning strategy was proposed which employs the rule models and Beyes presentation and combines forward chaining and backward chaining. [展开更多
A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the...A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.展开更多
The NEE\|striking Altyn Tagh Fault (ATF) has been well known as one major point to know the growth history of the Tibetan plateau. Lots of investigations done since 1970’s were mostly focus on active features, partic...The NEE\|striking Altyn Tagh Fault (ATF) has been well known as one major point to know the growth history of the Tibetan plateau. Lots of investigations done since 1970’s were mostly focus on active features, particularly on determining slip, slip rate and their distribution along the fault. However, Cenozoic slip\|history of this fault remains poorly understood, and the age of initiation and total offset are controversial. Several Cenozoic sedimentary basins develop in Suo’erkulinan to Mangya regions (Fig.1). Their sedimentary processes are closely related with the ATF. The studies of the Neogene sedimentary sequences and the reconstruction of the paleo\|geography are essential to establish the displacement history of the fault during Late Cenozoic.Located at the southern side of the ATF, the Suo’erkulinan basin consists of more than 600\|meter\|thick Pliocene Shizigou Formation below and about 120\|meter\|thick Early to Middle Pleistocene Qigequan Formation above according to the 1∶200000 geological map by Xinjiang Province. An obvious erosional surface can be seen on the top of the lower sequence. Sediments in the Shizigou Formation are characterized by 400\|meter\|thick yellow to red cobble\|sized conglomerates in the bottom, up\|grading to sandstones and grey\|green mudstones. This indicated that the sedimentary facies changed from alluvial fan to fluvial fan and sediments became more and more mature. The upper sequence, the Qigequan Formation, corresponds to an alluvial facies series composed of yellow to white cobble\|sized conglomerates intercalated with lenticular sandstones. Paleo\|current indicators showed that the Shizhigou conglomeratic series were sourced from northwest. Well\|developed syn\|sedimentary faults, normal faults mostly inherited from syn\|sedimentary faults, and some striation lineations on the surface indicated transtensional tectonic environment of the strike\|slip faulting.展开更多
This article studies the fault recorder in power system and introduces the Comtrade format. Andituses C++ programming to read recorded fault data and adopts Fourier analysis and symmetrical component method to filter ...This article studies the fault recorder in power system and introduces the Comtrade format. Andituses C++ programming to read recorded fault data and adopts Fourier analysis and symmetrical component method to filter and extract fundamental waves. Finally the effectiveness of the data processing method introduced in this paper is verified by CAAP software.展开更多
In this paper, a kind of data analysis method is used to research the space time coherence of Xianshuihe fault zone in the process of seimogeny. It is proved that, the space time coherence of Xianshuihe fault zone, ...In this paper, a kind of data analysis method is used to research the space time coherence of Xianshuihe fault zone in the process of seimogeny. It is proved that, the space time coherence of Xianshuihe fault zone, in the process of seismogeny, really exists. The greater the earthquake magnitude of a fault section, the greater is its influence on the 'neighboring' sections. To a strike fault zone which bears compressive stress, the fault sections are 'series connected'. That is to say, once an earthquake happens on a fault section, the time of seismogeny in the neighboring fault sections will be shortened, therefore, this earthquake has 'an effect of hastening the occurrence of earthquakes in neighboring fault sections'. It is an inevitable rule for the Xianshuihe fault zone that the earthquake quiescent period and the earthquake active period occur alternatively. To the Xianshuihe fault zone, the slip predictable model does not tally with the actual situation, while the improved time predictable model tallies with the fact quite well. The data analysis method used here, which is used to examine the earthquake prediction models, is put forward on the basis of historical earthquake records. This kind of method and the relative conclusions have important reference value on the establishment of earthquake prediction models.展开更多
Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators an...Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators and sensors. However, despite of the hardware redundancy, sensor faults have often affected the wind turbine normal operation and thus caused energy generation loss. In this paper, aiming at the wind turbine hydraulic pitch system, data-driven design of process monitoring(PM) and diagnosis has been realized in the wind turbine benchmark. Fault tolerant control(FTC) strategies focused on sensor faults have also been presented here, where with the implementation of soft sensor the sensor fault can be handled and the performance of the system is improved. The performance of this method is demonstrated with the wind turbine benchmark provided by Math Works.展开更多
The auto body process monitoring and the root cause diagnosis based on data-driven approaches are vital ways to improve the dimension quality of sheet metal assemblies. However, during the launch time of the process m...The auto body process monitoring and the root cause diagnosis based on data-driven approaches are vital ways to improve the dimension quality of sheet metal assemblies. However, during the launch time of the process mass production with an off-line measurement strategy, the traditional statistical methods are difficult to perform process control effectively. Based on the powerful abilities in information fusion, a systematic Bayesian based quality control approach is presented to solve the quality problems in condition of incomplete dataset. For the process monitoring, a Bayesian estimation method is used to give out-of-control signals in the process. With the abnormal evidence, the Bayesian network(BN) approach is employed to identify the fixture root causes. A novel BN structure and the conditional probability training methods based on process knowledge representation are proposed to obtain the diagnostic model. Furthermore, based on the diagnostic performance analysis, a case study is used to evaluate the effectiveness of the proposed approach. Results show that the Bayesian based method has a better diagnostic performance for multi-fault cases.展开更多
In order to diagnose gear shifting process in automated manual transmission（AMT）,a semi-quantitative signed directed graph（SDG）model is applied.Mathematical models are built by analysis of the power train dynamic ...In order to diagnose gear shifting process in automated manual transmission（AMT）,a semi-quantitative signed directed graph（SDG）model is applied.Mathematical models are built by analysis of the power train dynamic and the gear shifting control process.The SDG model is built based on related priori knowledge.By calculating the fuzzy membership degree of each compatible passway and its possible fault source,we get the possibilities of failure for each possible fault source.We begin with the nodes with the maximum possibility of failure in order to find the failed part.The diagnosis example shows that it is feasible to use the semi-quantitative SDG model for fault diagnosis of the gear shifting process in AMT.展开更多
文摘A fault processing policy was proposed for the circumstance under which all the dual-redundant closed-loop feedback control sensors went wrong.This policy was based on dual-redundant control law and may maintain the performance of turbofan engines.When pair of controlling sensors went wrong and the primary control law was unable to implement,control system performs the backup control law instead of the primary one so that the fault sensors were isolated from the closed control loop.This fault processing policy may not only avoid increasing engine weight and cost with the additional sensors hardware,but also avoid the error of analytical sensor signal.It can improve the engine mission reliability and control quality.
基金partly supportedby National Natural Science Foundation of China(Grant No.41472103)
文摘Recent studies, focused on dihedral angles and intersection processes, have increased understandings of conjugate fault mechanisms. We present new 3-D seismic data and microstructural core analysis in a case study of a large conjugate strike-slip fault system from the intracratonic Tarim Basin, NW China. Within our study area, ＂X＂ type NE and NW trending faults occur within Cambrian- Ordovician carbonates. The dihedral angles of these conjugate faults have narrow ranges, 19~ to 62~ in the Cambrian and 26~ to 51~ in the Ordovician, and their modes are 42~ and 44~ respectively. These data are significantly different from the ~60~ predicted by the Coulomb fracture criterion. It is concluded that： （1） The dihedral angles of the conjugate faults were not controlled by confining pressure, which was low and associated with shallow burial; （2） As dihedral angles were not controlled by pressure they can be used to determine the shortening direction during faulting; （3） Sequential slip may have played an important role in forming conjugate fault intersections; （4） The conjugate fault system of the Tarim basin initiated as rhombic joints; these subsequently developed into sequentially active ＂X＂ type conjugate faults; followed by preferential development of the NW-trending faults; then reactivation of the NE trending faults. This intact rhombic conjugate fault system presents new insights into mechanisms of dihedral angle development, with particular relevance to intracratonic basins.
文摘This paper presents a fault diagnosis method for process faults and sensor faults in a class of nonlinear uncertain systems.The fault detection and isolation architecture consists of a fault detection estimator and a bank of adaptive isolation estimators,each corresponding to a particular fault type.Adaptive thresholds for fault detection and isolation are presented.Fault detectability conditions characterizing the class of process faults and sensor faults that are detectable by the presented method are derived.A simulation example of robotic arm is used to illustrate the effectiveness of the fault diagnosis method.
基金Supported by the National Natural Science Foundation of China(21706291,61751305)
文摘Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.
基金Supported by National Basic Research Program of China（973 Program）（2012CB720505） the Fundamental Research Funds for the Central Universities（2012QNA5012）+1 种基金 Project of Education Department of Zhejiang Province（Y201223159） Technology Foundation for Selected Overseas Chinese Scholar of Zhejiang Province
文摘Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.
基金Supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (No. 2005-29)Excellent Scholar Research Award Foundation of Shandong Province (No. 2006BS05005)
文摘<Abstract>A novel fault detection and diagnosis method was proposed, using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur. It aims at parameter failures, and the parameters are updated via on-line correction. As it can predict the trend of process and determine the existence of malfunctions simultaneously, this method does not need to design problem-specific observer to estimate unmeasured state variables. Application of the proposed method is presented on one water tank and one aromatization reactor, and the results are compared with those from the traditional method.
基金Supported by the National Natural Science Foundation of China(61273160,61403418)the Natural Science Foundation of Shandong Province(ZR2011FM014)+1 种基金the Fundamental Research Funds for the Central Universities(10CX04046A)the Doctoral Fund of Shandong Province(BS2012ZZ011)
文摘Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis(LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis(PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.
文摘Abstract The Nansha ultra-crust layer-block is confined by ultra-crustal boundary faults of distinctive features, bordering the Kangtai-Shuangzi-Xiongnan extensional faulted zone on the north, the Baxian-Baram-Yoca-Cuyo nappe faulted zone on the south, the Wan'an-Natuna strike-slip tensional faulted zone on the west and the Mondoro-Panay strike-slip compressive faulted zone on the east. These faults take the top of the Nansha asthenosphere as their common detachmental surface. The Cenozoic dynamic process of the ultra-crust layer-block can be divided into four stages: K2-E21, during which the northern boundary faults extended, this ultra-crust layer-block was separated from the South China-Indosinian continental margin, the Palaeo-South China Sea subducted southwards and the Sibu accretion wedge was formed; E22-E31, during which the Southwest sub-sea basin extended and orogeny was active due to the collision of the Sibu accretion wedge; E32-N11, during which the central sub-sea basin extended, the Miri accretion wedge was formed and “A-type” subduction of the southern margin of the north Balawan occurred; N12-the present, during which large-scale thrusting and napping of the boundary faults in the south and mountain-building have taken place and the South China Sea stopped its extension.
基金Supported by the National Natural Science Foundation of China(21576143).
文摘A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.
文摘Leaching process is the first step in zinc hydrometallurgy, which involves the complex chemical reactions for dissolving zinc bearing material in dilute sulfuric acid. Ensuring the safe running of the process is a key point in the operation. An expert fault diagnosis system for the leaching process was proposed, which has been implemented in a nonferrous metals smeltery. The system architecture and the diagnosis procedure were presented, and the rule models with the certainty factor were constructed based on the empirical knowledge, empirical data and statistical results on past fault countermeasures, and an expert reasoning strategy was proposed which employs the rule models and Beyes presentation and combines forward chaining and backward chaining. [
基金Supported by the National Natural Science Foundation of China(61374140)Shanghai Pujiang Program(12PJ1402200)
文摘A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.
基金theprogramsof ( 1)theYoungGeologistsFoundationoftheMGMR (No .Qn979812 ) ( 2 )theNational(No .G19980 4 0 80 0 ) and ( 3)the
文摘The NEE\|striking Altyn Tagh Fault (ATF) has been well known as one major point to know the growth history of the Tibetan plateau. Lots of investigations done since 1970’s were mostly focus on active features, particularly on determining slip, slip rate and their distribution along the fault. However, Cenozoic slip\|history of this fault remains poorly understood, and the age of initiation and total offset are controversial. Several Cenozoic sedimentary basins develop in Suo’erkulinan to Mangya regions (Fig.1). Their sedimentary processes are closely related with the ATF. The studies of the Neogene sedimentary sequences and the reconstruction of the paleo\|geography are essential to establish the displacement history of the fault during Late Cenozoic.Located at the southern side of the ATF, the Suo’erkulinan basin consists of more than 600\|meter\|thick Pliocene Shizigou Formation below and about 120\|meter\|thick Early to Middle Pleistocene Qigequan Formation above according to the 1∶200000 geological map by Xinjiang Province. An obvious erosional surface can be seen on the top of the lower sequence. Sediments in the Shizigou Formation are characterized by 400\|meter\|thick yellow to red cobble\|sized conglomerates in the bottom, up\|grading to sandstones and grey\|green mudstones. This indicated that the sedimentary facies changed from alluvial fan to fluvial fan and sediments became more and more mature. The upper sequence, the Qigequan Formation, corresponds to an alluvial facies series composed of yellow to white cobble\|sized conglomerates intercalated with lenticular sandstones. Paleo\|current indicators showed that the Shizhigou conglomeratic series were sourced from northwest. Well\|developed syn\|sedimentary faults, normal faults mostly inherited from syn\|sedimentary faults, and some striation lineations on the surface indicated transtensional tectonic environment of the strike\|slip faulting.
文摘This article studies the fault recorder in power system and introduces the Comtrade format. Andituses C++ programming to read recorded fault data and adopts Fourier analysis and symmetrical component method to filter and extract fundamental waves. Finally the effectiveness of the data processing method introduced in this paper is verified by CAAP software.
文摘In this paper, a kind of data analysis method is used to research the space time coherence of Xianshuihe fault zone in the process of seimogeny. It is proved that, the space time coherence of Xianshuihe fault zone, in the process of seismogeny, really exists. The greater the earthquake magnitude of a fault section, the greater is its influence on the 'neighboring' sections. To a strike fault zone which bears compressive stress, the fault sections are 'series connected'. That is to say, once an earthquake happens on a fault section, the time of seismogeny in the neighboring fault sections will be shortened, therefore, this earthquake has 'an effect of hastening the occurrence of earthquakes in neighboring fault sections'. It is an inevitable rule for the Xianshuihe fault zone that the earthquake quiescent period and the earthquake active period occur alternatively. To the Xianshuihe fault zone, the slip predictable model does not tally with the actual situation, while the improved time predictable model tallies with the fact quite well. The data analysis method used here, which is used to examine the earthquake prediction models, is put forward on the basis of historical earthquake records. This kind of method and the relative conclusions have important reference value on the establishment of earthquake prediction models.
基金the National Natural Science Foundation of China(No.51205018)the Fundamental Research Funds for the Central Universities of China(No.FRF-TP-14-121A2)the Research Project of State Key Laboratory of Mechanical System and Vibration(No.MSV-2014-09)
文摘Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators and sensors. However, despite of the hardware redundancy, sensor faults have often affected the wind turbine normal operation and thus caused energy generation loss. In this paper, aiming at the wind turbine hydraulic pitch system, data-driven design of process monitoring(PM) and diagnosis has been realized in the wind turbine benchmark. Fault tolerant control(FTC) strategies focused on sensor faults have also been presented here, where with the implementation of soft sensor the sensor fault can be handled and the performance of the system is improved. The performance of this method is demonstrated with the wind turbine benchmark provided by Math Works.
基金the National Natural Science Foundation of China(Nos.51405299 and 51175340)the Natural Science Foundation of Shanghai(No.14ZR1428700)
文摘The auto body process monitoring and the root cause diagnosis based on data-driven approaches are vital ways to improve the dimension quality of sheet metal assemblies. However, during the launch time of the process mass production with an off-line measurement strategy, the traditional statistical methods are difficult to perform process control effectively. Based on the powerful abilities in information fusion, a systematic Bayesian based quality control approach is presented to solve the quality problems in condition of incomplete dataset. For the process monitoring, a Bayesian estimation method is used to give out-of-control signals in the process. With the abnormal evidence, the Bayesian network(BN) approach is employed to identify the fixture root causes. A novel BN structure and the conditional probability training methods based on process knowledge representation are proposed to obtain the diagnostic model. Furthermore, based on the diagnostic performance analysis, a case study is used to evaluate the effectiveness of the proposed approach. Results show that the Bayesian based method has a better diagnostic performance for multi-fault cases.
基金Supported by the Basic Research Foundation of Beijing Institute of Technology(20130342035)
文摘In order to diagnose gear shifting process in automated manual transmission（AMT）,a semi-quantitative signed directed graph（SDG）model is applied.Mathematical models are built by analysis of the power train dynamic and the gear shifting control process.The SDG model is built based on related priori knowledge.By calculating the fuzzy membership degree of each compatible passway and its possible fault source,we get the possibilities of failure for each possible fault source.We begin with the nodes with the maximum possibility of failure in order to find the failed part.The diagnosis example shows that it is feasible to use the semi-quantitative SDG model for fault diagnosis of the gear shifting process in AMT.
