Jiusun Zeng

Detecting abnormal situations using the Kullback-Leibler divergence

This article develops statistics based on the Kullback-Leibler (KL) divergence to monitor large-scale technical systems. These statistics detect anomalous system behavior by comparing estimated density functions for the current process behavior with reference density functions. For Gaussian distributed process variables, the paper proves that the difference in density functions, measured by the KL divergence, is a more sensitive measure than existing work involving multivariate statistics. To cater for a wide range of potential application areas, the paper develops monitoring concepts for linear static systems, that can produce Gaussian as well as non-Gaussian distributed process variables. Using recorded data from a glass melter, the article demonstrates the increased sensitivity of the KL-based statistics by comparing them to competitive ones.

Novel Just-In-Time Learning-Based Soft Sensor Utilizing Non-Gaussian Information

This brief develops a novel just-in-time (JIT) learning-based soft sensor for modeling of industrial processes. The recorded data is assumed to exhibit non-Gaussian signal components, which are extracted by a non-Gaussian regression (NGR) technique. Unlike previous work on JIT modeling which uses distance-based similarity measure for local modeling, this brief introduces a new similarity measure for the extracted non-Gaussian components using support vector data description. Based on the similarity measure, a JIT modeling procedure called NGR_JIT is proposed. Application studies on a numerical example as well as an industrial process demonstrate the proposed soft sensor can give better predictive accuracy when the predictor and response sets are non-Gaussian distributed.

Soft sensor development using non-Gaussian Just-In-Time modeling

This paper introduces a novel Just-In-Time (JIT) learning based soft sensor for modeling of non-Gaussian process. Most of JIT modeling uses distance based similarity measure for local modeling, which may be inappropriate for many industrial processes exhibiting non-Gaussian behaviors. Since most of industrial processes are non-Gaussian, a non-Gaussian regression (NGR) technique is used to extract non-Gaussian independent components that are correlated to response variable in the sense of mutual information. Support vector data description (SVDD) is then performed on the extracted independent components to construct a new similarity measure. Based on the similarity measure, a novel JIT modeling procedure is proposed. Application studies on a numerical example as well as an industrial process confirm that the proposed JIT model can achieve good predictive accuracy.

Statistical Monitoring of Industrial Process Faults Using Local Independent Component Regression

Abstract This article utilizes the independent component regression (ICR) algorithm which is capable of extracting non-Gaussian components from both input and output variables for monitoring of complex industrial systems. The components extracted by ICR are relevant in the sense of of mutual information rather than second order statistics. Statistical local approach is then incorporated into the ICR algorithm to monitor changes in process and model parameters. The proposed monitoring strategy is capable of detecting non-Gaussian parameter changes and the monitoring task is simplified by establishing low dimensional Gaussian monitoring statistics. An application study to fluidized bed reactor shows that the proposed monitoring strategy are efficient in detecting process faults.

CHAOTIC FEATURE OF MARTIN PROCESS IMPOSED ON THE COSINE FUNCTION

By analyzing the phase diagram of Martin process on the cosine function, it is shown that with the change of system parameters the system will eventually converge to a chaotic attractor. The process is repeated and stable focus, period doubling bifurcation occurs during this process. Further computation gives the maximum Lyapunov exponent of the system and meanwhile, the bifurcation diagram is drawn. Thus it is proved from theory that the system exhibits strong chaotic properties.