For critical engineering systems such as aircraft and aerospace vehicles, accurate Remaining Useful Life(RUL) prediction not only means cost saving, but more importantly, is of great significance in ensuring system re...For critical engineering systems such as aircraft and aerospace vehicles, accurate Remaining Useful Life(RUL) prediction not only means cost saving, but more importantly, is of great significance in ensuring system reliability and preventing disaster. RUL is affected not only by a system's intrinsic deterioration, but also by the operational conditions under which the system is operating. This paper proposes an RUL prediction approach to estimate the mean RUL of a continuously degrading system under dynamic operational conditions and subjected to condition monitoring at short equi-distant intervals. The dynamic nature of the operational conditions is described by a discrete-time Markov chain, and their influences on the degradation signal are quantified by degradation rates and signal jumps in the degradation model. The uniqueness of our proposed approach is formulating the RUL prediction problem in a semi-Markov decision process framework, by which the system mean RUL can be obtained through the solution to a limited number of equations. To extend the use of our proposed approach in real applications, different failure standards according to different operational conditions are also considered. The application and effectiveness of this approach are illustrated by a turbofan engine dataset and a comparison with existing results for the same dataset.展开更多
We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional va...We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional value-at-risk for random immediate reward variables in Markov decision processes, under whose risk measure criteria the risk-optimal policies are characterized by the optimality equations for the discounted or average case. As an application, the inventory models are considered.展开更多
In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the PO...In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the POMDP framework. The elements of the POMDP model are analyzed and described. The state transfer law in the model can be described by the method of interactive multiple model (IMM) due to the diversity of the target motion law, which is used to switch the motion model to accommodate target maneuvers, and hence improving the tracking accuracy. The simulation results show that the model can achieve efficient planning for the UAV route, and effective tracking for the target. Furthermore, the path planned by this model is more reasonable and efficient than that by using the single state transition law.展开更多
Considering the dynamic character of repeated games and Markov process, this paper presented a novel dynamic decision model for symmetric repeated games. In this model, players＇ actions were mapped to a Markov decisi...Considering the dynamic character of repeated games and Markov process, this paper presented a novel dynamic decision model for symmetric repeated games. In this model, players＇ actions were mapped to a Markov decision process with payoffs, and the Boltzmann distribution was intousluced. Our dynamic model is different from others＇ , we used this dynamic model to study the iterated prisoner＇ s dilemma, and the results show that this decision model can successfully be used in symmetric repeated games and has an ability of adaptive learning.展开更多
基金the National Natural science Foundation of China (No. 71701008) for supporting this research
文摘For critical engineering systems such as aircraft and aerospace vehicles, accurate Remaining Useful Life(RUL) prediction not only means cost saving, but more importantly, is of great significance in ensuring system reliability and preventing disaster. RUL is affected not only by a system's intrinsic deterioration, but also by the operational conditions under which the system is operating. This paper proposes an RUL prediction approach to estimate the mean RUL of a continuously degrading system under dynamic operational conditions and subjected to condition monitoring at short equi-distant intervals. The dynamic nature of the operational conditions is described by a discrete-time Markov chain, and their influences on the degradation signal are quantified by degradation rates and signal jumps in the degradation model. The uniqueness of our proposed approach is formulating the RUL prediction problem in a semi-Markov decision process framework, by which the system mean RUL can be obtained through the solution to a limited number of equations. To extend the use of our proposed approach in real applications, different failure standards according to different operational conditions are also considered. The application and effectiveness of this approach are illustrated by a turbofan engine dataset and a comparison with existing results for the same dataset.
文摘We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional value-at-risk for random immediate reward variables in Markov decision processes, under whose risk measure criteria the risk-optimal policies are characterized by the optimality equations for the discounted or average case. As an application, the inventory models are considered.
基金supported by the Aeronautical Science Foundation of China(20135153031 20135553035 2017ZC53033)
文摘In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the POMDP framework. The elements of the POMDP model are analyzed and described. The state transfer law in the model can be described by the method of interactive multiple model (IMM) due to the diversity of the target motion law, which is used to switch the motion model to accommodate target maneuvers, and hence improving the tracking accuracy. The simulation results show that the model can achieve efficient planning for the UAV route, and effective tracking for the target. Furthermore, the path planned by this model is more reasonable and efficient than that by using the single state transition law.
基金We also acknowledge the support by the National Natural Science Foundation of China （Grant No. 60574071）.
文摘Considering the dynamic character of repeated games and Markov process, this paper presented a novel dynamic decision model for symmetric repeated games. In this model, players＇ actions were mapped to a Markov decision process with payoffs, and the Boltzmann distribution was intousluced. Our dynamic model is different from others＇ , we used this dynamic model to study the iterated prisoner＇ s dilemma, and the results show that this decision model can successfully be used in symmetric repeated games and has an ability of adaptive learning.
