During the compaction of a road subgrade, the mechanical parameters of the soil mass change in real time, but current research assumes that these parameters remain unchanged. In order to address this discrepancy, this...During the compaction of a road subgrade, the mechanical parameters of the soil mass change in real time, but current research assumes that these parameters remain unchanged. In order to address this discrepancy, this paper establishes a relationship between the degree of compaction K and strain ε. The relationship between the compaction degree K and the shear strength of soil(cohesion c and frictional angle φ) was clearly established through indoor experiments. The subroutine UMAT in ABAQUS finite element numerical software was developed to realize an accurate calculation of the subgrade soil compaction quality. This value was compared and analyzed against the assumed compaction value of the model, thereby verifying the accuracy of the intelligent compaction calculation results for subgrade soil. On this basis, orthogonal tests of the influential factors(frequency, amplitude, and quality) for the degree of compaction and sensitivity analysis were carried out. Finally, the ‘acceleration intelligent compaction value’, which is based on the acceleration signal, is proposed for a compaction meter value that indicates poor accuracy. The research results can provide guidance and basis for further research into the accurate control of compaction quality for roadbeds and pavements.展开更多
In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learni...In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learning algorithms suffer the slow convergence rate because of the enormous learning space produced by joint-action. In this article, a prediction-based reinforcement learning algorithm is presented for multi-agent cooperation tasks, which demands all agents to learn predicting the probabilities of actions that other agents may execute. A multi-robot cooperation experiment is run to test the efficacy of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation policy much faster than the primitive reinforcement learning algorithm.展开更多
This paper focuses on the development of an embedded integrated servo-controller (EISC) for servomotors. Comprising of mainly servo-controller and servo-amplifiers, this EISC is capable of controlling a wide range o...This paper focuses on the development of an embedded integrated servo-controller (EISC) for servomotors. Comprising of mainly servo-controller and servo-amplifiers, this EISC is capable of controlling a wide range of servomotors to perform complieated tasks. Hence, integration of this EISC with a servomotor forms an intelligent modular actuator (IMA) that is essential to modern manufacturing industries. The development of such an EISC involves two major tasks: first, designing the hardware of a compact-sized and highly compatible EISC, and second, developing the software functions to facilitate its functionalities and capahilities. The developed EISC hence forms an integrated-servo-eontrol module, which determines the capability, functionality, flexibility and responsiveness of these IMAs.展开更多
A method to set up 3D collar prototype is developed in this paper by using the technique of cubic spline and bicubic surface patch. Then the relationship between the parameters of 3D collar prototype and different col...A method to set up 3D collar prototype is developed in this paper by using the technique of cubic spline and bicubic surface patch. Then the relationship between the parameters of 3D collar prototype and different collar styles are studied. Based on the relationship, we can develop some algorithms of transferring style requirements to the parameters value of the collar prototype, and obtain some generation rules for the design of 3D collar style. As such, the knowledge base can be constructed, and the intelligent design system of 3D collar style is built. Using the system, various 3D collar styles can be designed automatically to satisfy various style requirements.展开更多
Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a C...Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a CNC machine tool is the main source of the big data on which a CPS model is established. In this work-process model, a method based on instruction domain is applied to analyze the electronic big data, and a quantitative description of the numerical control(NC) processes is built according to the G code of the processes. Utilizing the instruction domain, a work-process CPS model is established on the basis of the accurate, real-time mapping of the manufacturing tasks, resources, and status of the CNC machine tool. Using such models, case studies are conducted on intelligent-machining applications, such as the optimization of NC processing parameters and the health assurance of CNC machine tools.展开更多
In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user pro...In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body's minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.展开更多
A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The st...A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training.展开更多
The task assignment problem of robots in a smart warehouse environment (TARSWE) based on cargo-to-person is investigated. Firstly, the sites of warehouse robots and the order picking tasks are given and the task ass...The task assignment problem of robots in a smart warehouse environment (TARSWE) based on cargo-to-person is investigated. Firstly, the sites of warehouse robots and the order picking tasks are given and the task assignment problem for picking one order is formulated into a mathematical model to minimize the total operation cost. Then a heuristic algorithm is designed to solve the task assignment problem for picking multiple orders. Finally, simulations are done by using the orders data of online bookstore A. The results show that using the heuristic algorithm of this paper to assign robots, the cost was reduced by 2% and it can effectively avoid far route and unbalanced workload of robots. The feasibility and validity of the model and algorithm are verified. The model and algorithm in this paper provide a theoretical basis to solve the TARSWE.展开更多
To solve the problem of data recovery on free disk sectors, an approach of data recovering based on intelligent pattern matching is proposed in this paper. Different from the methods based on the file directory, this ...To solve the problem of data recovery on free disk sectors, an approach of data recovering based on intelligent pattern matching is proposed in this paper. Different from the methods based on the file directory, this approach utilizes the consistency among the data on the disk. A feature pattern library is established based on different types of fries according to the internal constructions of text. Data on sectors will be classified automatically by data clustering and evaluating. When the conflict happens on data classification, the digestion will be initiated by adopting context pattern. Based on this approach, the paper achieved the data recovery system aiming at pattern matching of txt, word and PDF fries. Raw and formatting recovery tests proved that the system works well.展开更多
This paper shows detailed steps for modeling a quadcopter with Euler-Lagrange equations, followed by controlling it with intelligent control that includes states decoupling. In addition, this control shows non-convent...This paper shows detailed steps for modeling a quadcopter with Euler-Lagrange equations, followed by controlling it with intelligent control that includes states decoupling. In addition, this control shows non-conventional membership functions for the most instable states, in order to get a fast and effective response.展开更多
A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules ...A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of The simulat two linguistic variables with the degree of certainty and the storage structure of rule base was described. on results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5 %.展开更多
Foraging behavior in ant colonies has come to be viewed as a prototypical example to describe how complex group behavior can arise from simple individuals. In order to research the feature of self-organization in swar...Foraging behavior in ant colonies has come to be viewed as a prototypical example to describe how complex group behavior can arise from simple individuals. In order to research the feature of self-organization in swarm intelligence (SI), a mean field model is given and analyzed in foraging process with three sources in this paper. The distance of trails and the richness of each source are considered. Both of the theoretical numerical analysis and Monte Carlo simulation show the power law relationship between the completion time and the flux of foragers. The work presented here guides a better understanding on self-organization and swarm intelligence. It can be used to design more efficient, adaptive, and reliable intelligent systems.展开更多
Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on f...Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.展开更多
With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbul...With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.展开更多
The smart cable with embedded distributed fiber optical Bragg grating (FBG) sensors was chosen as the object to study a new diagnosis method about broken wires of the bridge cable. The diagnosis strategy based on ca...The smart cable with embedded distributed fiber optical Bragg grating (FBG) sensors was chosen as the object to study a new diagnosis method about broken wires of the bridge cable. The diagnosis strategy based on cable force and stress distribution state of steel wires was put forward. By establishing the bridge-cable and cable-steel wires model, the broken wires sample database was simulated numerically. A method of the characterization cable state pattern which can both represent the degree and location of broken wires inside a cable was put forward. The training and predicting results of the sample database by the back propagation (BP) neural network showed that the proposed broken wires diagnosis method was feasible and expanded the broken wires diagnosis research area by using the smart cable which was used to be only representing cable force.展开更多
基金Project(51878164) supported by the National Natural Science Foundation of ChinaProjects(BK20161421, BK20140109) supported by the Natural Science Foundation of Jiangsu Province, China+4 种基金Project(141076) supported by the Huoyingdong Foundation of the Ministry of Education of ChinaProject(BZ2017011) supported by the Science and Technology Support Project of Jiangsu Province, ChinaProject(2242015R30027) supported by the Fundamental Research Funds for the Central Universities, ChinaProject(grant number KFJ170106) supported by the Changsha University of Science & Technology via Open Fund of National Engineering Laboratory of Highway Maintenance Technology, ChinaProject(2018B51) supported by the Science and Technology Support Project of Qilu Transportation Development Group, China。
文摘During the compaction of a road subgrade, the mechanical parameters of the soil mass change in real time, but current research assumes that these parameters remain unchanged. In order to address this discrepancy, this paper establishes a relationship between the degree of compaction K and strain ε. The relationship between the compaction degree K and the shear strength of soil(cohesion c and frictional angle φ) was clearly established through indoor experiments. The subroutine UMAT in ABAQUS finite element numerical software was developed to realize an accurate calculation of the subgrade soil compaction quality. This value was compared and analyzed against the assumed compaction value of the model, thereby verifying the accuracy of the intelligent compaction calculation results for subgrade soil. On this basis, orthogonal tests of the influential factors(frequency, amplitude, and quality) for the degree of compaction and sensitivity analysis were carried out. Finally, the ‘acceleration intelligent compaction value’, which is based on the acceleration signal, is proposed for a compaction meter value that indicates poor accuracy. The research results can provide guidance and basis for further research into the accurate control of compaction quality for roadbeds and pavements.
文摘In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learning algorithms suffer the slow convergence rate because of the enormous learning space produced by joint-action. In this article, a prediction-based reinforcement learning algorithm is presented for multi-agent cooperation tasks, which demands all agents to learn predicting the probabilities of actions that other agents may execute. A multi-robot cooperation experiment is run to test the efficacy of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation policy much faster than the primitive reinforcement learning algorithm.
基金Supported by the High Technology Research and Development Programme of China (No.2002AA421160) and the National Natural Science Foundation of China (No.50375008).
文摘This paper focuses on the development of an embedded integrated servo-controller (EISC) for servomotors. Comprising of mainly servo-controller and servo-amplifiers, this EISC is capable of controlling a wide range of servomotors to perform complieated tasks. Hence, integration of this EISC with a servomotor forms an intelligent modular actuator (IMA) that is essential to modern manufacturing industries. The development of such an EISC involves two major tasks: first, designing the hardware of a compact-sized and highly compatible EISC, and second, developing the software functions to facilitate its functionalities and capahilities. The developed EISC hence forms an integrated-servo-eontrol module, which determines the capability, functionality, flexibility and responsiveness of these IMAs.
文摘A method to set up 3D collar prototype is developed in this paper by using the technique of cubic spline and bicubic surface patch. Then the relationship between the parameters of 3D collar prototype and different collar styles are studied. Based on the relationship, we can develop some algorithms of transferring style requirements to the parameters value of the collar prototype, and obtain some generation rules for the design of 3D collar style. As such, the knowledge base can be constructed, and the intelligent design system of 3D collar style is built. Using the system, various 3D collar styles can be designed automatically to satisfy various style requirements.
基金support of the studies is from the National Major Scientific and Technological Special Project for "Development and comprehensive verification of complete products of open high-end CNC system, servo device and motor" (2012ZX04001012)
文摘Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a CNC machine tool is the main source of the big data on which a CPS model is established. In this work-process model, a method based on instruction domain is applied to analyze the electronic big data, and a quantitative description of the numerical control(NC) processes is built according to the G code of the processes. Utilizing the instruction domain, a work-process CPS model is established on the basis of the accurate, real-time mapping of the manufacturing tasks, resources, and status of the CNC machine tool. Using such models, case studies are conducted on intelligent-machining applications, such as the optimization of NC processing parameters and the health assurance of CNC machine tools.
文摘In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body's minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.
基金Funded by the Open Research Fund Program of GIS Laboratory of Wuhan University (No. wd200609).
文摘A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training.
基金Project Supported: National Natural Science Foundation of China (11131009, 71540028, F012408), Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (CIT&TCD20130327), and major research project of Beijing Wuzi University.
文摘The task assignment problem of robots in a smart warehouse environment (TARSWE) based on cargo-to-person is investigated. Firstly, the sites of warehouse robots and the order picking tasks are given and the task assignment problem for picking one order is formulated into a mathematical model to minimize the total operation cost. Then a heuristic algorithm is designed to solve the task assignment problem for picking multiple orders. Finally, simulations are done by using the orders data of online bookstore A. The results show that using the heuristic algorithm of this paper to assign robots, the cost was reduced by 2% and it can effectively avoid far route and unbalanced workload of robots. The feasibility and validity of the model and algorithm are verified. The model and algorithm in this paper provide a theoretical basis to solve the TARSWE.
文摘To solve the problem of data recovery on free disk sectors, an approach of data recovering based on intelligent pattern matching is proposed in this paper. Different from the methods based on the file directory, this approach utilizes the consistency among the data on the disk. A feature pattern library is established based on different types of fries according to the internal constructions of text. Data on sectors will be classified automatically by data clustering and evaluating. When the conflict happens on data classification, the digestion will be initiated by adopting context pattern. Based on this approach, the paper achieved the data recovery system aiming at pattern matching of txt, word and PDF fries. Raw and formatting recovery tests proved that the system works well.
文摘This paper shows detailed steps for modeling a quadcopter with Euler-Lagrange equations, followed by controlling it with intelligent control that includes states decoupling. In addition, this control shows non-conventional membership functions for the most instable states, in order to get a fast and effective response.
基金Project(50374079) supported bythe National Natural Science Foundation of China project(2002cB312200) supported bythe State Key Fundamental Research and Development Programof China
文摘A fast generation method of fuzzy rules for flux optimization decision-making was proposed in order to extract the linguistic knowledge from numerical data in the process of matter converting. The fuzzy if-then rules with consequent real number were extracted from numerical data, and a linguistic representation method for deriving linguistic rules from fuzzy if-then rules with consequent real numbers was developed. The linguistic representation consisted of The simulat two linguistic variables with the degree of certainty and the storage structure of rule base was described. on results show that the method involves neither the time-consuming iterative learning procedure nor the complicated rule generation mechanisms, and can approximate complex system. The method was applied to determine the flux amount of copper converting furnace in the process of matter converting. The real result shows that the mass fraction of Cu in slag is reduced by 0.5 %.
基金Sponsored by the National High Technology Research and Development Program 863(Grant No.2009AA04Z215)the National Natural Science Foundation of China(Grant No.60975071)the Fund for Basic Research from Harbin Engineering University(Grant No.002060260750)
文摘Foraging behavior in ant colonies has come to be viewed as a prototypical example to describe how complex group behavior can arise from simple individuals. In order to research the feature of self-organization in swarm intelligence (SI), a mean field model is given and analyzed in foraging process with three sources in this paper. The distance of trails and the richness of each source are considered. Both of the theoretical numerical analysis and Monte Carlo simulation show the power law relationship between the completion time and the flux of foragers. The work presented here guides a better understanding on self-organization and swarm intelligence. It can be used to design more efficient, adaptive, and reliable intelligent systems.
基金supported by the Major State Basic Research Development of China (Grant No. 2011CB706803)National Natural Science Foundation of China (Grant No. 50875098)Important National Science & Technology Specific Projects of China (Grant No. 2009ZX04014-024)
文摘Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.
基金supported by the National Natural Science Foundation of China(Grant Nos.92152301,and 91852115)the National Numerical Wind tunnel Project(Grand No.NNW2018-ZT1B01).
文摘With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.
基金The research work reported in this paper was supported by the National Engineering Laboratory for Fiber Optic Sensing Technology, Wuhan University of Technology, China. Thanks for the support of the Fundamental Research Funds for the Central Universities (WUT: 2014-IV-090) and the National Natural Science Foundation of China (Major Program: 61290310). Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
文摘The smart cable with embedded distributed fiber optical Bragg grating (FBG) sensors was chosen as the object to study a new diagnosis method about broken wires of the bridge cable. The diagnosis strategy based on cable force and stress distribution state of steel wires was put forward. By establishing the bridge-cable and cable-steel wires model, the broken wires sample database was simulated numerically. A method of the characterization cable state pattern which can both represent the degree and location of broken wires inside a cable was put forward. The training and predicting results of the sample database by the back propagation (BP) neural network showed that the proposed broken wires diagnosis method was feasible and expanded the broken wires diagnosis research area by using the smart cable which was used to be only representing cable force.