In the existing quality-related fault detection methods,the fault signal is prone to be covered up by the random trend of non-stationary variables,which leads to low fault detection rate.To solve the problem,a quality-related fault detection method based on Cointegration Analysis and Modified Projection to Latent Structures (CA-MPLS) is proposed.Firstly,the stationary feature trend among non-stationary variables is extracted by using cointegration analysis.Then,the stationary feature information and stationary variables are fused to construct orthogonal projection space,and the process variables are orthogonally projected into quality-related subspace and quality-unrelated subspace.Finally,the corresponding statistical indicators are designed in the two subspaces for online monitoring.Simulation results from Tennessee Eastman demonstrate the effectiveness of the proposed method in significantly reducing the influence of non-stationary features and enhancing fault detection accuracy.