Cheng Wei, Zhang Zhousuo, Cao Hongrui, He Zhengjia, Zhu Guanwen
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2014 Apr 25;14(5):7625-46. doi: 10.3390/s140507625.
This paper investigates one eigenvalue decomposition-based source number estimation method, and three information-based source number estimation methods, namely the Akaike Information Criterion (AIC), Minimum Description Length (MDL) and Bayesian Information Criterion (BIC), and improves BIC as Improved BIC (IBIC) to make it more efficient and easier for calculation. The performances of the abovementioned source number estimation methods are studied comparatively with numerical case studies, which contain a linear superposition case and a both linear superposition and nonlinear modulation mixing case. A test bed with three sound sources is constructed to test the performances of these methods on mechanical systems, and source separation is carried out to validate the effectiveness of the experimental studies. This work can benefit model order selection, complexity analysis of a system, and applications of source separation to mechanical systems for condition monitoring and fault diagnosis purposes.
本文研究了一种基于特征值分解的源数估计方法，以及三种基于信息的源数估计方法，即赤池信息准则（AIC）、最小描述长度（MDL）和贝叶斯信息准则（BIC），并将BIC改进为改进的BIC（IBIC），使其计算效率更高、更简便。通过数值案例研究对上述源数估计方法的性能进行了比较研究，其中包括一个线性叠加案例和一个线性叠加与非线性调制混合案例。构建了一个具有三个声源的试验台来测试这些方法在机械系统上的性能，并进行源分离以验证实验研究的有效性。这项工作有助于模型阶次选择、系统复杂性分析以及源分离在机械系统状态监测和故障诊断中的应用。
