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The damage level assessment of equipment function is an important part of equipment battle damage assessment. In practice, it is often difficult to obtain accurate damage level assessment results due to a lack of damage test data and insufficient modeling. Aiming at this problem, a functional damage assessment method based on Bayesian networks and transfer learning is proposed in the case of small sample test data. First, a Bayesian network model considering the correlation of component damage is constructed, which can more accurately reflect the damage results of equipment when incomplete damage information is obtained. Then, an improved TrAdaboost transfer learning method is proposed for the Bayesian network model, which overcomes the disadvantage that the traditional TrAdaboost method is unable to transfer the results with randomization. Finally, the method proposed in this paper is applied to the Asia network and a certain type of radar vehicle functional damage level assessment process, and the results prove the effectiveness and superiority of the proposed method.

Abstract With the further development of service-oriented, performance-based contracting (PBC) has been widely adopted in industry and manufacturing. However, maintenance optimization problems under PBC have not received enough attention. To further extend the scope of PBC’s application 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 to maximize the profit and improve system performance at a relatively low cost under PBC. In the proposed CBM model, the first inspection interval has been considered in order to reduce the inspection frequency and the cost rate. Then, a particle swarm algorithm (PSO) and related solution procedure are presented to solve the multiple decision variables in our proposed model. In the end, a numerical example is provided so as to demonstrate the superiority of the presented model. By comparing the proposed policy with the conventional ones, the superiority of our proposed policy is proved, which can bring more profits to providers and improve performance. Sensitivity analysis is conducted in order to research the effect of corrective maintenance cost and time required for corrective maintenance on optimization policy. A comparative study is given to illustrate the necessity of distinguishing the first inspection interval or not.