As the key component of a servo motor, the torque and temperature rise of the brake at the operating temperature affect the production quality of injection molding machines and other equipment. To achieve application of the brake and evaluate its performance at a high operating temperature, a high-torque permanent magnet brake with an axial disc structure is proposed. The permanent magnet consists of six small magnets instead of the traditional monolithic ring magnet. The key parameters of the coil are designed, and the magneto-thermal coupling method is established. The magneto-thermal coupling method considers the effect of temperature on material properties and feeds the temperature back to the electromagnetic field to correct the resistance, permeability, remanence and other coefficients. It then updates the heat source of the temperature field. The temperature rise is calculated iteratively between the electromagnetic field and the temperature field. The simulation results of the one-way method and the magneto-thermal coupling method are obtained and compared with the experimental results. The evaluation errors of the magneto-thermal coupling method for temperature and braking torque are 1.9% and 4.7% respectively, which are lower than the errors of the one-way method.
<abstract><p>Permanent magnet brake (PMB) is a safe and effective braking mechanism used to stop and hold the load in place. Due to its complex structure and high reliability, assessing the reliability of PMB remains a challenge. The main difficulty lies in that there are several performance indicators reflecting the health state of PMB, and they are correlated with each other. In order to assess the reliability of PMB more accurately, a constant stress accelerated degradation test (ADT) is carried out to collect degradation data of two main performance indicators in PMB. An accelerated bivariate Wiener degradation model is proposed to analyse the ADT data. In the proposed model, the relationship between degradation rate and stress levels is described by Arrhenius model, and a common random effect is introduced to describe the unit-to-unit variation and correlation between the two performance indicators. The Markov Chain Monte Carlo (MCMC) algorithm is performed to obtain the point and interval estimates of the model parameters. Finally, the proposed model and method are applied to analyse the accelerated degradation data of PMB, and the results show that the reliability of PMB at the used condition can be quantified quite well.</p></abstract>
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