A new fault detection method for non-Gaussian process based on robust independent component analysis

Conventional fault detection method based on fast independent component analysis (FastICA) is sensitive to outliers in the modeling data and thus may perform poorly under the adverse effects of outliers. To solve such problem, a new fault detection method for non-Gaussian process based on robust independent component analysis (RobustICA) is proposed in this paper. A RobustICA algorithm which can effectively reduce the effects of outliers is firstly developed to estimate the mixing matrix and extract non-Gaussian feature called independent components (ICs) by robust whitening and robust determination of the maximum non-Gaussian directions. Furthermore, a monitoring statistic for each extracted IC is constructed to detect process faults. Simulations on a simple example of the mixing matrix estimation and a fault detection example in the continuous stirred tank reactor system demonstrate that the RobustICA achieves much higher estimation accuracy for the mixing matrix and the ICs than the commonly used FastICA algorithm, and the RobustICA-based fault detection method outperforms the conventional FastICA-based fault detection method in terms of the fault detection time and fault detection rate.     

结构振动疲劳加速试验技术研究

目的提出一种用于评估随机振动环境下工程结构长期耐久性和疲劳可靠性的加速试验技术。方法通过开展一系列高斯和非高斯振动疲劳对比试验,系统研究影响结构振动疲劳寿命的各种因素,包括随机振动激励的均方根值、功率谱密度、带宽和峭度值等。结果非高斯随机振动激励的带宽和峭度值对结构振动疲劳寿命也有明显影响。结论当结构振动激励呈现明显的非高斯特征时,设计随机振动疲劳加速试验方案必须综合考虑振动激励的带宽和峭度值。