Subspace fault detection method based on independent component contribution
一种基于独立元贡献度的子空间故障检测方法

In order to handle the problem of fault detection for industrial processes, an improved subspace method is proposed based on the definitions of the independent component (IC) contribution and the contribution matrix. First, the appropriate independent components are extracted by independent component analysis(ICA), and then, the contributions of different independent components on the process variables are calculated to construct the contribution matrix. According to the contribution, a set of suitable subspaces, which can reflect different root causes, are constructed by corresponding variables. The fault detection models are established on these subspaces. Finally, combining all the above fault detection models and choosing the proper ensemble strategy based on the actual requirement or the spread characteristic of the faults, we make the ensemble decision for the fault detection of industrial processes. A case study on the Tennessee Eastman(TE) process for each mode (1 normal and 21 faulty) illustrates the effectiveness of the proposed method.