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| Fatigue Failure Prediction Method of Sintered NdFeB Material Based on Particle Filter |
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Abstract A novel method of predicting sintered NdFeB fatigue damage prediction based on particle filter and micro-magnetic monitoring against the urgency and uncertainty during the sintered NdFeB service is put forward. First, in order to accurately reflect the dynamic change relationship between system state variables and output variables, the force-magnetic coupling model of sintered NdFeB is introduced into the particle filtering theoretical framework. Furthermore, the particle filtering evolution equation based on relative permeability is designed. According to the changeable magnetic induction intensity signal during the fatigue damage of sintered NdFeB obtained from the micro-magnetic monitoring system, the particle filtering observation equation based on magnetic induction intensity is constructed. It means that the system status variable was characterized by input variables and output variables. Then, the sintered NdFeB fatigue damage prediction process based on particle filtering algorithm is realized by associating the evolution equation and the observation equation. By monitoring the magnetic induction intensity signal of the stress concentrated region, the characteristic relationship between stress and magnetic induction intensity in sintered NdFeB can be established. Therefore, it can dynamically detect and monitor the fatigue damage of NdFeB by observing the changes of magnetic induction intensity signals. Finally, in order to verify the feasibility and effectiveness of the fatigue failure process of sintered NdFeB by using particle filter method, fatigue experimental study on sintered NdFeB magnets were performed. The experimental results show that the estimation precision and accuracy rating of predicting the sintered NdFeB evolution of fatigue damage is high. Compared with the particle filter prediction and support vector machine, when the fatigue load is 120MPa, the RMSE (Root Mean Square Error) of the particle filter prediction result
is only 76.69. And at this time the result of particle filter prediction is 1/4 of support vector machine. This research provides a new idea for predicting the fatigue failure of brittle sintered magnets.
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Received: 15 June 2021
Published: 19 April 2022
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2022, Vol. 43 
