For the fault detection and diagnosis problem in largescale industrial systems,there are two important issues: the missing data samples and the non-Gaussian property of the data.However,most of the existing data-driven methods cannot be able to handle both of them.Thus,a new Bayesian network classifier based fault detection and diagnosis method is proposed.At first,a non-imputation method is presented to handle the data incomplete samples,with the property of the proposed Bayesian network classifier,and the missing values can be marginalized in an elegant manner.Furthermore,the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures,so that the Bayesian network can process the non-Gaussian data in an effective way.Therefore,the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way.The diagnosis results are expressed in the manner of probability with the reliability scores.The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process.The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements. For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues: the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods can not be able to handle both of them .Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values ​​can be The marginalized in an elegant manner. Future Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Wherefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probab ility with the reliability scores.The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.