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In this paper, a new sparse dynamic principal component analysis (SDPCA) technique is proposed, which combines two popular monitoring methods, dynamic principal component analysis (DPCA) and sparse principal component analysis (SPCA). The proposed SDPCA is used for fault detection for industrial process. In the proposed SDPCA method, the sparse loading vectors are derived by solving an optimization problem through the dynamic augmented matrix of process data. SDPCA technique not only considers the temporal correlation of process data, but also reduces redundancy of the process data, meantime reduces the computation load. Moreover, we will discuss a new forward selection algorithm for determining the number of non-zero loadings. The proposed SDPCA method is assessed through a numerical example and Tennessee Eastman benchmark process. Results show that the SDPCA based fault detection method could obtain a better performance compared with PCA, DPCA and SPCA based methods. Key words：principal component analysis; sparse principal component analysis; dynamic principal component analysis; non-zero loading; Fault detection