Most existing algorithms for identifying multi-model system are based on minimizing the square of bias between global outputs of the actual system and the identified model, but the resultant model lacks of robustness....Most existing algorithms for identifying multi-model system are based on minimizing the square of bias between global outputs of the actual system and the identified model, but the resultant model lacks of robustness. In order to solve this problem, this paper considers some other algorithms in which local models are identified independently and presents a multi-model identification algorithm based on weighted cost function, which uses the idea of local weighted regression and local approximation while keeps the model structure of global identification algorithm. The result of application to a 300MW unit boiler superheater illustrates that the multi-model generated by the proposed algorithm has better trade-off between global fitting and local interpretation.展开更多
在噪声干扰较强的环境下,为了克服傅里叶分解方法(Fourier Decomposition Method,FDM)在分析调制信号及单独使用调制信号双谱(Modulated Signal Bispectrum,MSB)在分析非平稳信号方面的不足,提出了一种FDM和MSB相结合的滚动轴承故障诊...在噪声干扰较强的环境下,为了克服傅里叶分解方法(Fourier Decomposition Method,FDM)在分析调制信号及单独使用调制信号双谱(Modulated Signal Bispectrum,MSB)在分析非平稳信号方面的不足,提出了一种FDM和MSB相结合的滚动轴承故障诊断方法。首先,使用FDM按照高频到低频的方式搜寻傅里叶固有模态函数分量(Fourier Intrinsic band Functions,FIBFs);以加权峭度指标作为评判标准,对信号进行重构,确保得到最佳的信号;然后对新的信号利用MSB分析方法进行解调处理,最终通过复合切片谱实现故障特征频率的提取。最后,通过上述方法对模拟信号和滚动轴承外圈故障信号进行分析,其研究结果表明:该方法能够有效地提取故障特征频率,并且与常规双谱进行对比,验证所提方法的优越性。展开更多
文摘Most existing algorithms for identifying multi-model system are based on minimizing the square of bias between global outputs of the actual system and the identified model, but the resultant model lacks of robustness. In order to solve this problem, this paper considers some other algorithms in which local models are identified independently and presents a multi-model identification algorithm based on weighted cost function, which uses the idea of local weighted regression and local approximation while keeps the model structure of global identification algorithm. The result of application to a 300MW unit boiler superheater illustrates that the multi-model generated by the proposed algorithm has better trade-off between global fitting and local interpretation.
文摘在噪声干扰较强的环境下,为了克服傅里叶分解方法(Fourier Decomposition Method,FDM)在分析调制信号及单独使用调制信号双谱(Modulated Signal Bispectrum,MSB)在分析非平稳信号方面的不足,提出了一种FDM和MSB相结合的滚动轴承故障诊断方法。首先,使用FDM按照高频到低频的方式搜寻傅里叶固有模态函数分量(Fourier Intrinsic band Functions,FIBFs);以加权峭度指标作为评判标准,对信号进行重构,确保得到最佳的信号;然后对新的信号利用MSB分析方法进行解调处理,最终通过复合切片谱实现故障特征频率的提取。最后,通过上述方法对模拟信号和滚动轴承外圈故障信号进行分析,其研究结果表明:该方法能够有效地提取故障特征频率,并且与常规双谱进行对比,验证所提方法的优越性。
