Fault detection of the system has a very important role in industrial production. Modified partial least squares (MPLS) is an extended algorithm based on PLS, which has a good detection effect in quality-related fault detection. However, when the test data contains quality-unrelated faults, the MPLS algorithm has a high fault missed alarm rates. In addition, the fault false alarm rates of the MPLS will increase because of its static threshold, and these problems have a great influence on industrial process monitoring. To this end, this paper proposes a quality-related fault detection method based on local information increment and MPLS (LII-MPLS). On the basis of the MPLS, the fault missed alarm rates of quality-related fault is significantly reduced by using local information incremental technology to update and detect the test data in real time. Meanwhile, the complexity of the process results in static control limits that cannot meet the needs of fault detection and existing dynamic control limits have certain limitations, therefore, this paper improves the static control limit and generalizes it as a local dynamic threshold. Finally, the effectiveness of the proposed approach is verified on an industrial benchmark of Tennessee Eastman process.