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In order to solve the problem of a lack of supportive means for evaluating the resilience of battle damage equipment, a Bayesian network cloud model is proposed to evaluate the resilience of battle damage equipment. The equipment functional features are analyzed to establish the equipment functional state evaluation model. Moreover, the samples of Bayesian network parameters training are obtained by inserting the results of battle damage simulation into the functional evaluation model. The simulation flow of parts state recovery probability is designed to determine the relationship between parts’ functional state and time. Based on the cloud model, the transformation model of functional state level probability to functional index is established. Hence, the equipment functional state level probability obtained by Bayesian network reasoning is transformed into a functional index and the transformation from uncertainty to certainty is realized. Considering self-propelled artillery as the object of resilience evaluation, the results of numerical examples show that by this method, the problem of equipment resilience evaluation can be effectively solved, and more information can be obtained by the accurate representation method compared to the traditional Bayesian network probabilistic evaluation results. This is greatly significant to the wartime maintenance support decision.

As a new form of support contract, performance-based contracting (PBC) has been extensively adopted in industry and manufacturing recent years. However, maintenance optimization problems under PBC have not received enough attention. In order to further expand the application of PBC in the field of maintenance optimization, we investigate the condition-based maintenance (CBM) optimization for gamma deteriorating systems under PBC. Considering the repairable single-component system subject to the gamma degradation process, this paper proposes a CBM optimization model with an objective of maximizing the profit and improving system performance at a lower cost under PBC. In the proposed CBM model, first inspection interval is considered to reduce the inspection frequency and the cost. Then, a particle swarm algorithm (PSO) and related solution procedure are presented in order to solve the multiple decision variables in our proposed model. Finally, a numerical example is provided to illustrate the applicability and effectiveness of our proposed model. Through comparing the proposed policy with the conventional one, the superiority of our proposed policy is proved, which can bring more profits to providers and improve performance. Sensitivity analysis is conducted to investigate the effect of corrective maintenance cost and time required for corrective maintenance to optimization policy. Comparative study is given to illustrate the necessity of distinguishing first inspection interval and repeat inspection interval.

This paper explores the solutions to the problem of battlefield damage pattern recognition. The main research results of the two components of clustering and classification are discussed respectively, and the main characteristics and existing problems of the existing methods are analyzed. The advantages, disadvantages and application scope of different methods are described in detail, which provides a basic theoretical guidance for the study of battlefield damage identification.

In view of the problem that the randomness of maintenance time of functional units cannot be considered in traditional system maintainability evaluation, this paper firstly analyzes the general process of system task maintainability evaluation and determines the basic method of task maintainability evaluation. Then, aiming at the problem that it is difficult to solve the problem analytically when several functional units fail at the same time, a task maintainability evaluation algorithm based on simulation is proposed. Finally, an example is given to illustrate the effectiveness and practicability of the proposed method.