Statistical tolerance–cost–service life optimization of blade bearing of controllable pitch propeller considering the marine environment conditions through meta-heuristic algorithm

Abstract

Abstract Blade bearing is the key mechanism of the controllable pitch propellers (CPPs) to adjust the pitch to change the thrust. In the marine environment, wear and deformation of the contact surface of the blade bearing are quite large, which may result in leakage to reduce the service life of CPP. However, most studies on tolerance optimization of blade bearing disregard deformations and wear, and it could result in unreasonable tolerances and impair the CPP’s service life. In this paper, to establish the statistical tolerance–cost–service life optimization model of the blade bearing of the CPP considering the marine environment effects in propellers, a prediction model about the relationship between the thrust and rotation speed of the CPP considering environment conditions of wind, wave, and ocean currents is constructed. Based on this, the wear and deformation are simulated according to the predicted rotation speed in the marine environment. Considering the deformation and wear in the marine environment, a tolerance analysis model of the assembly deviations of the blade bearing is introduced, and the assembly characteristics and functional requirements are established and converted into probability constraints to establish the service-life model based on statistical tolerance analysis and Monte Carlo simulation methods. Afterward, a statistical tolerance–cost–service life optimization model is established and transformed into a single-objective optimization problem through the normalization method and the weighted sum method, and solved by a meta-heuristic algorithm named cuckoo search. Finally, the application of the proposed model is demonstrated through a case involving the blade bearing of a CPP, and the optimized results show that the manufacturing cost of the blade bearing is reduced and its service life is extended.