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This paper studies the fault prediction problem of permanent magnet fault-tolerant motor equipped in the electric actuation system, which is conducive to monitor aircraft health status accurately, and provide decision support for aircraft maintenance. For the characteristic signal of permanent magnet fault-tolerant motor system is complex and chaotic, the traditional grey model prediction accuracy is not high. Thus, a new improved forecast method based on grey theory is proposed in the paper. Firstly, we use a logarithmic transformation to deal with the original fault feature sequence, and then to deal with the sequence via accumulation generation of first degree. After establish the GM(1,1) grey model, fitting the data back into analog value, we can get the prediction data. The results show that the original sequence is processed by logarithmic transformation, the prediction accuracy increased to 96.5% above, the prediction error is reduced by 4.63% than the primitive sequence basic model, the fault prediction of the behavior is improved effectively.