Self-Learning of Decision Rules for Process Control

点击翻译按钮获取中文摘要

The objective of this project is to develop a widely-applicable, automated method to learn decision rules from the data in a sensor network. The decision rules that are learned will detect anomalies from the normal operating environment when neither the normal operations nor the anomalies to be detected are pre-specified. The learner will also incorporate categorical as well as numerical data, identify contributors to a decision-rule signal when it occurs, and adapt the rules over time as requirements for the sensor system change. Basically, it will adapt itself to the characteristics of the normal environment, handle categorical and numerical data, self-train, and then detect anomalies. Measures of variable importance will be developed to provide guidance to diagnose a detected signal. Such measures will also be used to adaptively to improve the number of variables that can be incorporated into a solution. Preliminary experiments will be expanded to test the learner with simulated and real data. The learner will be compared to traditional, optimal solutions when they exist and then evaluated under more stressful conditions in which traditional solutions fail.Success of this project will provide network intelligence that is inexpensive and easy to install, maintain, and mange. These results can be used to eventually develop an embedded module (intelligence) that can be made available in sensor-network infrastructures. The flexibility of the derived decision rules can incorporate highly nonlinear models. This flexibility and the conceptual simplicity of the approach, along with the computational resources now widely available, can generate a surge of interest in this automated approach. The methods can be useful to manufacturing and in areas such as biological, civil, and transportation monitoring. Also, the measures for variable importance can be applied to interrogate other learning algorithms in complex applications.