The partial least squares algorithm(PLS) is an effective method, which has been widely used in multivariate statistical processes. Efficient projection to latent structures(EPLS) is an improved PLS algorithm proposed in recent years and has a good detection effect in quality-related fault detection. However, when the test data has a quality-independent failure, the false alarm rate of the EPLS algorithm is high, which may lead to false alarms and has a great influence on fault detection in industrial processes. In order to reduce the false alarm rate of detection-independent faults, four OSC-EPLS algorithms are proposed in combination with four orthogonal signal corrections(OSC) and the EPLS. In this paper, we establish an OSC model with quality-independent fault samples to preprocess on-line inspection data, and use the EPLS algorithm for fault detection to reduce the false alarm rate. Finally, combined with Tennessee Eastman industrial process, four kinds of the OSC-EPLS, PLS, and EPLS algorithms are used for fault detection. The false alarm rate is compared with the effective alarm rate to reflect the advantages of the proposed algorithm in fault detection.