Reliability prediction of crane brakes based on the bayesian method

Crane brakes have limited failure samples, and the braking torque degradation quantity exhibits randomness; thus, traditional prediction methods are unsuitable for reliability prediction of crane brakes. The braking torque is a measurable physical quantity used to characterize braking performance. Therefore, this paper takes the braking torque degradation quantity as the entry point and, based on Bayesian statistical principles, derives a reliability prediction model for the brake. First, using experimental data from the development stage as prior information, the joint prior distribution form of the braking torque degradation quantity parameters was derived, and the hyperparameters in the distribution were determined. Second, by incorporating on-site small samples of experimental data, the corresponding posterior distribution function and the maximum a posteriori estimates values of the parameters are derived. Finally, by incorporating the reliability function of the Weibull distribution, the time for the braking torque degradation quantity to decrease from 0 to the specified threshold at a given confidence level is obtained, enabling the reliability prediction of the brake. The comparison results indicate that the proposed method demonstrates good applicability and accuracy in reliability prediction of crane brakes, providing a theoretical basis for their reliability prediction and maintenance. This study provides an effective approach for reliability prediction of mechanical equipment based on small-sample experimental data.