奇异值趋势分解在燃油泵性能指数预测中的应用

Application of singular value trend decomposition in prediction of fuel pump performance index

机载燃油泵的性能退化数据呈现出平稳—加速—平稳的多阶段模式。针对燃油泵性能退化数据具有非线性、非平稳的特征,提出了一种基于奇异值趋势分解的组合预测方法。利用奇异值趋势分解提取原始数据的趋势项和去趋势项;分别对趋势项和去趋势项构建最小二乘支持向量机(LSSVM)模型,并采用粒子群优化(PSO)算法实现LSSVM参数的自适应优化选择。将趋势项和去趋势项的预测结果进行组合以得到性能数据的预测结果。将该方法应用于机载燃油泵实验数据并进行对比分析,结果表明:所提方法能够有效地提取趋势信息,实现性能数据的预测。

The performance degradation of airborne fuel pump is multi-stage with stationary-accelerated-stationary degradation pattern.Aiming at performance index having characteristic of non-linear and non-stationary,a combined predicting method based on singular value decomposition(SVD)is presented.SVD method is used to decompose and extract trend and de-trend items of original data.Least square support vector machine(LSSVM)model of trend and de-trend is build.Particle swarm optimization(PSO)is applied into adaptive selection of the best parameter of LSSVR.Each prediction result is summed to obtain result of performance data prediction.The proposed method is applied to the experimental data.The analysis results show that the proposed approach can effectively extract the trend information and achieve prediction of performance data.