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.

为解决惯性测试转台精度退化状态缺乏有效评价方法的问题，提出了基于隐马尔可夫模型（HMM）和粒子滤波的测试转台位置精度退化趋势预测模型。 （PF）算法。PF算法的初始参数通过粒子群优化（PSO）算法进行了优化。选择振动信号作为研究数据，可以从转台精度退化的速度测试中获得。首先，利用整体经验模态分解和主成分分析（EEMD-PCA）算法对原始振动信号进行去噪，提取具有故障特征的信号进行信号重建。其次，利用统计特征值作为观测矩阵可以训练HMM模型，获得早期位置精度下降的诊断和健康状态指标。最后，利用PF算法可以建立测试转台精度下降的预测模型，并计算出测试转台精度的剩余使用寿命。以第50组数据为预测起点，预测的剩余使用寿命为21年，实际测量结果为17年，彼此接近。将模型计算结果与测试测量结果进行比较，表明该模型可以有效，准确地预测测试转台精度的变化趋势和剩余使用寿命。