A unified diagnostic framework via symmetrized data aggregation

In statistical process control of high-dimensional data streams, in addition to online monitoring of abnormal changes, fault diagnosis of responsible components has become increasingly important. Existing diagnostic procedures have been designed for some typical models with distribution assumptions. Moreover, there is a lack of systematic approaches to provide a theoretical guarantee of significance in estimating shifted components. In this article, we introduce a new procedure to control the False Discovery Rate (FDR) of fault diagnosis. The proposed method formulates the fault diagnosis as a variable selection problem and utilizes the symmetrized data aggregation technique via sample splitting, data screening, and information pooling to control the FDR. Under some mild conditions, we show that the proposed method can achieve FDR control asymptotically. Extensive numerical studies and two real-data examples demonstrate satisfactory FDR control and remarkable diagnostic power in comparison to existing methods.