Precision degradation prediction of inertial test turntable based on Hidden Markov Model and optimized particle filtering

Abstract

In order to solve the problem that there is no effective evaluation method for the precision degradation state of inertial test turntable, a prediction model for the position precision degradation trend of test turntable was proposed based on the Hidden Markov Model (HMM) algorithm and Particle Filter (PF) algorithm. The initial parameter of the PF algorithm was optimized by the Particle Swarm Optimization (PSO) algorithm. The vibration signal was selected as the research data, which could be obtained from an velocity test of turntable precision degradation. Firstly, the original vibration signal was denoised by Ensemble Empirical Mode Decomposition and Principal Component Analysis (EEMD-PCA) algorithm, and the signal with fault characteristic was extracted for signal reconstruction; Secondly, a HMM model could be trained by using the statistical characteristic values as observation matrix, and the diagnosis of early position precision degradation and the health state indexes could be obtained. Finally, a prediction model of the test turntable precision degradation could be established by using PF algorithm, and the Remaining Useful Life (RUL) of the test turntable precision could be calculated. When the 50th group data were taken as the prediction starting point, the predicted remaining useful life was 21 years, and the actual measured result was 17 years, which are close to each other. Comparing the model calculation results and the test measurement results, it is shown that the model could effectively and accurately predict the change trend and remaining useful life of the test turntable precision.