This paper presents an adaptive sliding window recursive sparse principal component analysis method for the on-line fault monitoring of time-varying industrial processes. Firstly

feature information of normal process data space is extracted by the sliding window

and the sparse principal component analysis is applied to the current window block matrix to construct the sparse principal component analysis-based process fault monitoring model. Then

the forgetting factor is adjusted in real time according to the similarities of adjacent windows to update the sliding window size adaptively

so that the sparse principal component fault monitoring model can effectively track the time-varying process. Finally

the sparse load matrix of the sliding window is renewed recursively to update the fault monitoring model dynamically. Fault monitoring results of the nonlinear numerical simulation system and the Tennessee-Eastman process show that the proposed method can effectively improve the fault detection accuracy and adapt to the on-line fault monitoring of long process industries with time-varying processes.