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Maintenance is inevitable for repairable components or systems in modern industries. Since the maintenance effectiveness has a great influence on the subsequent operations and in addition, different maintenance options are possible for the components of the system during the break between any two successive missions, the optimal selective maintenance strategy needs to be determined for a system performing successive missions. A number of selective maintenance models were set up on the basis that the durations of each mission are predetermined, the maintenance time is negligible, and the states of the components at the end of the previous mission can be accurately obtained. However, in the actual industrial and military missions, these premises may not always hold. In this paper, a novel selective maintenance model under uncertainties and limited maintenance time is proposed to improve these deficiencies. The genetic algorithm is selected to solve the optimization problem, and an illustrative example is presented to demonstrate the proposed method. The optimal selective maintenance decision without the constraint of maintenance time is used for comparison.

Fuzzy multi-state systems (FMSSs) exist widely in practical engineering. It is usually difficult to evaluate the reliability of FMSSs because the reliability data is usually fuzzy due to the inaccuracy or imperfection of information, and there is often correlation between the main components and others which constitute the FMSSs. Although many research works with respect to the independent failure of components have been carried out, the master-slave relationship between the main components and others of the FMSSs is often ignored, thus unrealistic results are often obtained with this treatment. Based on fuzzy universal generating function (FUGF), an effective reliability analysis method of FMSSs considering the correlation and fuzziness is proposed in this paper. In the novel method, the fuzziness of reliability data and the master-slave relationship between the main and other components are taken into consideration, and the performance levels and corresponding probabilities of the non-main components are considered as conditional probability distributions. A case study with respect to the reliability analysis of hydraulic system is presented to illustrate the application of the proposed method. INDEX TERMS Multi-state system (MSS), fuzzy universal generating function (FUGF), composition operator, three-leg robot.

To improve the predictive ability in trajectory of large unmanned aerial vehicle (UAV) and the calculation performance in complicated circumstances with mixed airspace, multiple aircraft types, and joint operations, the concept of phased trajectory deviation (PTD) is introduced and a corresponding minimal interval algorithm (PTD-MI) is set up. This algorithm is capable of deriving the minimal interval between various aircraft types according to the crosswind impacts and the UAV characteristics at different flight phases and thus achieves the effective safety evaluation in airspace operation. To demonstrate the rationality and generality of the proposed algorithm, several simulation experiments are conducted. Based on the experimental results, flight procedure protection area is plotted by PTD-MI algorithm and compared with that generated by Ground-Based Augmentation System (GBAS). Results indicate that the proposed algorithm is capable of deriving a more scientific basis for airspace assignment and outperform GBAS in dealing with wide-area space problems. And, compared with GBAS, PTD-MI algorithm shows a more stable calculation performance and is easier to output the results. PTD-MI algorithm is proposed under the flight safety regulation designed by the International Civil Aviation Organization (ICAO) and designed to provide effective technical supports for the safe and normal operations of aircrafts.

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The traditional fatigue life prediction methods based on the S-N curve all believe that the parameters in the model are deterministic constants and can be categorized to the deterministic life prediction. However, in practice, it is difficult to carry out a large number of experiments due to the limitation of time or the possible shortage of funds. In addition, the specimens used in the experiments are not exactly the same, and the test operations and data reading depend on the accuracy of the test equipment as well as the subjective judgment of the testers, which result to the uncertainty of the S-N curve. Therefore, the uncertainty should be considered in order to improve the accuracy of the fatigue life prediction. In this paper, the uncertain factors affecting the fatigue life of welded joints are summarized, and the generalized polynomial chaos (gPC) is introduced into fatigue life prediction. A novel probabilistic fatigue life prediction method combined with the nonlinear cumulative damage model considering the uncertainty of the S-N curve is constructed. An illustrative example is presented to demonstrate the advantages of the proposed approach.

In the reliability-based design optimization (RBDO) problems of structures or systems, only a single type of variable involving is rare, and most of time are hybrid variables. Random variables are often used to describe uncertainty, and these random variables can usually be described by certain probability distributions. However, in practice, the probability distributions of some random variables are difficult or even impossible to be obtained, or the obtained probability distributions may be inaccurate, and can only be roughly described by fuzzy values or intervals. Therefore, this paper is committed to solving RBDO problems under the hybrid of fuzzy variables and interval variables. Firstly, the RBDO model of the structure under the hybrid variables is established. Secondly, the fuzzy variables are transformed to random variables according to entropy invariance. Finally, RBDO is carried out based on sequential single-loop method in the worst case. A numerical example is presented to illustrate the feasibility of the proposed method.