The Multiple-model Adaptive Estimation method has low capability to track abrupt faults; therefore, multiple fading factors may result in diverging the Strong Tracking Filter. Moreover, the fault probability calculation is large. In this paper, an improved Strong Tracking Multiple-model Adaptive Estimation fast diagnosis algorithm is proposed. The tracking performance of the filter was improved by multiple fading factors. An improved renewal equation of the step prediction covariance matrix is proposed. The stability of the filter was guaranteed, and the estimation accuracy was improved. Based on the Euclidean norm, a fast fault isolation method that reduces the fault probability calculation is proposed. The simulation results show that this algorithm is more efficient and has a better performance.