The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuse...The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.展开更多
Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential te...Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy （DSTS） for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision pro- cess （POSMDP） is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process （BSMDP） through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run ex- pected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.展开更多
In this paper, we focus on the failure analysis of unmanned autonomous swarm(UAS) considering cascading effects. A framework of failure analysis for UAS is proposed.Guided by the framework, the failure analysis of UAS...In this paper, we focus on the failure analysis of unmanned autonomous swarm(UAS) considering cascading effects. A framework of failure analysis for UAS is proposed.Guided by the framework, the failure analysis of UAS with crash fault agents is performed. Resilience is used to analyze the processes of cascading failure and self-repair of UAS. Through simulation studies, we reveal the pivotal relationship between resilience, the swarm size, and the percentage of failed agents.The simulation results show that the swarm size does not affect the cascading failure process but has much influence on the process of self-repair and the final performance of the swarm.The results also reveal a tipping point exists in the swarm. Meanwhile, we get a counter-intuitive result that larger-scale UAS loses more resilience in the case of a small percentage of failed individuals, suggesting that the increasing swarm size does not necessarily lead to high resilience. It is also found that the temporal degree failure strategy performs much more harmfully to the resilience of swarm systems than the random failure. Our work can provide new insights into the mechanisms of swarm collapse, help build more robust UAS, and develop more efficient failure or protection strategies.展开更多
With continuous growth in scale,topology complexity,mission phases,and mission diversity,challenges have been placed for efficient capability evaluation of modern combat systems.Aiming at the problems of insufficient ...With continuous growth in scale,topology complexity,mission phases,and mission diversity,challenges have been placed for efficient capability evaluation of modern combat systems.Aiming at the problems of insufficient mission consideration and single evaluation dimension in the existing evaluation approaches,this study proposes a mission-oriented capability evaluation method for combat systems based on operation loop.Firstly,a combat network model is given that takes into account the capability properties of combat nodes.Then,based on the transition matrix between combat nodes,an efficient algorithm for operation loop identification is proposed based on the Breadth-First Search.Given the mission-capability satisfaction of nodes,the effectiveness evaluation indexes for operation loops and combat network are proposed,followed by node importance measure.Through a case study of the combat scenario involving space-based support against surface ships under different strategies,the effectiveness of the proposed method is verified.The results indicated that the ROI-priority attack method has a notable impact on reducing the overall efficiency of the network,whereas the O-L betweenness-priority attack is more effective in obstructing the successful execution of enemy attack missions.展开更多
The problem of sequential fault diagnosis is to construct a diagnosis tree that can isolate the failure sources with minimal test cost. Pervious sequential fault diagnosis strategy generating algorithms only consider ...The problem of sequential fault diagnosis is to construct a diagnosis tree that can isolate the failure sources with minimal test cost. Pervious sequential fault diagnosis strategy generating algorithms only consider the execution cost at application stage, which may result in a solution with poor quality from the view of life cycle cost. Furthermore, due to the fact that uncertain information exists extensively in the real-world systems, the tests are always imperfect. In order to reduce the cost of fault diagnosis in the realistic systems, the sequential fault diagnosis problem with imperfect tests considering life cycle cost is presented and formulated in this work, which is an intractable NP-hard AND/OR decision tree construction problem. An algorithm based on AND/OR graph search is proposed to solve this problem. Heuristic search based on information theory is applied to generate the sub-tree in the algorithm. Some practical issues such as the method to improve the computational efficiency and the diagnosis strategy with multi-outcome tests are discussed. The algorithm is tested and compared with previous algorithms on the simulated systems with different scales and uncertainty. Application on a wheel momentum system of a spacecraft is studied in detail. Both the simulation and application results suggest that the cost of the diagnosis strategy can be reduced significantly by using the proposed algorithm, especially when the placement cost of the tests constitutes a large part of the total cost.展开更多
The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibrat...The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.展开更多
The reliability evaluation of a multistate network is primarily based on d-minimal paths/cuts(d-MPs/d-MCs).However,being a nondeterminism polynomial hard(NP-hard)problem,searching for all d-MPs is a rather challenging...The reliability evaluation of a multistate network is primarily based on d-minimal paths/cuts(d-MPs/d-MCs).However,being a nondeterminism polynomial hard(NP-hard)problem,searching for all d-MPs is a rather challenging task.In existing implicit enumeration algorithms based on minimal paths(MPs),duplicate d-MP candidates may be generated.An extra step is needed to locate and remove these duplicate d-MP candidates,which costs significant computational effort.This paper proposes an efficient method to prevent the generation of duplicate d-MP candidates for implicit enumeration algorithms for d-MPs.First,the mechanism of generating duplicate d-MP candidates in the implicit enumeration algorithms is discussed.Second,a direct and efficient avoiding-duplicates method is proposed.Third,an improved algorithm is developed,followed by complexity analysis and illustrative examples.Based on the computational experiments comparing with two existing algorithms,it is found that the proposed method can significantly improve the efficiency of generating d-MPs for a particular demand level d.展开更多
基金supported by the National Natural Science Foundation of China(51175502)
文摘The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.
基金supported by the National Natural Science Foundation of China (51175502)
文摘Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy （DSTS） for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision pro- cess （POSMDP） is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process （BSMDP） through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run ex- pected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.
基金This work was supported by the Science and Technology on Reliability&Environmental Engineering Laboratory(6142004004-2)the Science Technology Commission of the CMC(2019-JCJQ-JJ-180,ZZKY-YX-10-3).
文摘In this paper, we focus on the failure analysis of unmanned autonomous swarm(UAS) considering cascading effects. A framework of failure analysis for UAS is proposed.Guided by the framework, the failure analysis of UAS with crash fault agents is performed. Resilience is used to analyze the processes of cascading failure and self-repair of UAS. Through simulation studies, we reveal the pivotal relationship between resilience, the swarm size, and the percentage of failed agents.The simulation results show that the swarm size does not affect the cascading failure process but has much influence on the process of self-repair and the final performance of the swarm.The results also reveal a tipping point exists in the swarm. Meanwhile, we get a counter-intuitive result that larger-scale UAS loses more resilience in the case of a small percentage of failed individuals, suggesting that the increasing swarm size does not necessarily lead to high resilience. It is also found that the temporal degree failure strategy performs much more harmfully to the resilience of swarm systems than the random failure. Our work can provide new insights into the mechanisms of swarm collapse, help build more robust UAS, and develop more efficient failure or protection strategies.
文摘With continuous growth in scale,topology complexity,mission phases,and mission diversity,challenges have been placed for efficient capability evaluation of modern combat systems.Aiming at the problems of insufficient mission consideration and single evaluation dimension in the existing evaluation approaches,this study proposes a mission-oriented capability evaluation method for combat systems based on operation loop.Firstly,a combat network model is given that takes into account the capability properties of combat nodes.Then,based on the transition matrix between combat nodes,an efficient algorithm for operation loop identification is proposed based on the Breadth-First Search.Given the mission-capability satisfaction of nodes,the effectiveness evaluation indexes for operation loops and combat network are proposed,followed by node importance measure.Through a case study of the combat scenario involving space-based support against surface ships under different strategies,the effectiveness of the proposed method is verified.The results indicated that the ROI-priority attack method has a notable impact on reducing the overall efficiency of the network,whereas the O-L betweenness-priority attack is more effective in obstructing the successful execution of enemy attack missions.
基金Project(C1320063131)supported by China Civil Space Foundation
文摘The problem of sequential fault diagnosis is to construct a diagnosis tree that can isolate the failure sources with minimal test cost. Pervious sequential fault diagnosis strategy generating algorithms only consider the execution cost at application stage, which may result in a solution with poor quality from the view of life cycle cost. Furthermore, due to the fact that uncertain information exists extensively in the real-world systems, the tests are always imperfect. In order to reduce the cost of fault diagnosis in the realistic systems, the sequential fault diagnosis problem with imperfect tests considering life cycle cost is presented and formulated in this work, which is an intractable NP-hard AND/OR decision tree construction problem. An algorithm based on AND/OR graph search is proposed to solve this problem. Heuristic search based on information theory is applied to generate the sub-tree in the algorithm. Some practical issues such as the method to improve the computational efficiency and the diagnosis strategy with multi-outcome tests are discussed. The algorithm is tested and compared with previous algorithms on the simulated systems with different scales and uncertainty. Application on a wheel momentum system of a spacecraft is studied in detail. Both the simulation and application results suggest that the cost of the diagnosis strategy can be reduced significantly by using the proposed algorithm, especially when the placement cost of the tests constitutes a large part of the total cost.
基金Projects(51375484,51475463)supported by the National Natural Science Foundation of ChinaProject(kxk140301)supported by Interdisciplinary Joint Training Project for Doctoral Student of National University of Defense Technology,China
文摘The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.
基金supported by the National Natural Science Foundation of China(71701207)the Science and Technology on Reliability&Environmental Engineering Laboratory(6142004004-2)the Science and Technology Commission of the CMC(2019-JCJQ-JJ-180)。
文摘The reliability evaluation of a multistate network is primarily based on d-minimal paths/cuts(d-MPs/d-MCs).However,being a nondeterminism polynomial hard(NP-hard)problem,searching for all d-MPs is a rather challenging task.In existing implicit enumeration algorithms based on minimal paths(MPs),duplicate d-MP candidates may be generated.An extra step is needed to locate and remove these duplicate d-MP candidates,which costs significant computational effort.This paper proposes an efficient method to prevent the generation of duplicate d-MP candidates for implicit enumeration algorithms for d-MPs.First,the mechanism of generating duplicate d-MP candidates in the implicit enumeration algorithms is discussed.Second,a direct and efficient avoiding-duplicates method is proposed.Third,an improved algorithm is developed,followed by complexity analysis and illustrative examples.Based on the computational experiments comparing with two existing algorithms,it is found that the proposed method can significantly improve the efficiency of generating d-MPs for a particular demand level d.
