基于多传感器粒子权重优化的序贯最大似然比故障诊断算法

针对多传感器量测下非线性系统故障诊断问题,给 出了一种基于多传感器粒子权重优化的序贯最大似然 比检验——故障诊断(SMLR-MWOPF)算法。首先,为改善量测随机噪声对粒子权重度量稳定 性不良影响,结合多源信息融合技术实现 对多传感器量测中冗余和互补信息的提取和利用,设计一种粒子权重优化策略；通过减小粒 子权重方差以改善粒 子权重的可靠性和稳定性,进而实现滤波器估计精度的提升。其次,结合序贯概率比检验(S PRT)和交互式多模型(IMM)框架构 建了一种基于残差检测的在线序贯最大似然比检验方法；另外,在结构上合理简化输入交互 和输出交互环节,以 降低粒子滤波(PF)在IMM模型结构实现中的计算复杂度。理论分析和仿真实验验证了算法的 可行性和有效性。

Sequential maximum likelihood ratio test for fault diagnosis based on multi-sen sor particle weight optimization

Aiming at the nonlinear system fault d iagnosis in multi-sensor observations,a novel sequential maximum likelihood rat io test for fault diagnosis based on multi-sensor particle weight optimization is proposed.Firstly,to improve the adverse effects on the stability of particle weight measurement caused by ra ndom noise,the particle weight optimization strategy is designed by multi-source information fusion technology to fully extract and exploit the redundancy and complementary information from multi-sensor observations.Its im plementation principle is to improve the reliability and stability of particle weight by the decline of particle weig ht variance,and to promote the estimation precision.Secondly,combined with the sequential probability ratio test and the i nteracting multi-model,a novel online sequential maximum likelihood ratio method based on residual test is presented.I n addition,considering the decline of computation complexity in the combination process of particle filter and interacting multi- model,the input interaction step and the output step are reasonably simplified in the construction of algorithm.The theo retical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.