Add time:08/13/2019         Source:sciencedirect.com

The analysis of rubber-accelerated aging test data provides a scientific basis for the prediction of rubber component storage life, optimal maintenance cycle development, equipment operation status monitoring and health management, and the identification of the acceleration factor is the key step. This study focuses on addressing the shortcomings of existing identification methods, such as cumbersome process, low efficiency and difficulty of ensuring accuracy. Accordingly, three types of modified particle swarm optimisation (MPSO) algorithm with an inertia weight variable are introduced for parameter identification based on the principle of time–temperature equivalence. Taking four sets of constant stress rubber-accelerated aging data at different high temperatures as an example, the acceleration factors of each high-temperature acceleration data relative to the reference temperature data are identified. The high-temperature data are translated by the time–temperature equivalent translation method, and nonlinear power exponential regression analysis is performed on the data after translation superposition. A parameter identification method based on the average absolute dispersion coefficient as the objective function and the MPSO algorithm for iterative optimisation is proposed. The acceleration factor at natural temperature is extrapolated from the identified high-temperature acceleration factors, and the degradation of the rubber-accelerated aging performance at room temperature is predicted and compared with the measured results. Results show that the predicted values are consistent with the measured values, and the accuracy of the parameter identification results is verified.

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