Missing Data Probability Estimation-Based Bayesian Outlier Detection for Plant-Wide Processes with Multisampling Rates

Traditional outlier detection methods assume that the sampling time and interval are the same. However, for plant-wide processes, since the signal change rate of different devices may vary by several orders of magnitude, the measured data in real-world systems usually have different sampling rates, resulting in missing data. To achieve reliable outlier detection, a missing data probability estimation-based Bayesian outlier detection method is adopted. In this strategy, the expectation–maximization (EM) algorithm is first used to estimate the likelihood probability of different evidence under different process statuses by using the history dataset which contains complete and incomplete samplings. Secondly, the realization of unavailable parts in the monitoring point is estimated as a probability through historical data and online moving horizon data. Bayesian theory and likelihood probability are then used to calculate the outlier posterior probability of different realization. Finally, the outlier probability of the monitoring sampling is calculated by the probability of different realizations and the corresponding outlier probability. Using the Tennessee Eastman (TE) dataset, a simulation indicates that the proposed method exhibits a significant improvement over the complete data method

Missing Data Probability Estimation-Based Bayesian Outlier Detection for Plant-Wide Processes with Multisampling Rates

Traditional outlier detection methods assume that the sampling time and interval are the same. However, for plant-wide processes, since the signal change rate of different devices may vary by several orders of magnitude, the measured data in real-world systems usually have different sampling rates, resulting in missing data. To achieve reliable outlier detection, a missing data probability estimation-based Bayesian outlier detection method is adopted. In this strategy, the expectation–maximization (EM) algorithm is first used to estimate the likelihood probability of different evidence under different process statuses by using the history dataset which contains complete and incomplete samplings. Secondly, the realization of unavailable parts in the monitoring point is estimated as a probability through historical data and online moving horizon data. Bayesian theory and likelihood probability are then used to calculate the outlier posterior probability of different realization. Finally, the outlier probability of the monitoring sampling is calculated by the probability of different realizations and the corresponding outlier probability. Using the Tennessee Eastman (TE) dataset, a simulation indicates that the proposed method exhibits a significant improvement over the complete data method