Showing papers by "Hao Luo published in 2014"

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

Abstract: Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization

Abstract: In this paper, two online schemes for an integrated design of fault-tolerant control (FTC) systems with application to Tennessee Eastman (TE) benchmark are proposed. Based on the data-driven design of the proposed fault-tolerant architecture whose core is an observer/residual generator based realization of the Youla parameterization of all stabilization controllers, FTC is achieved by an adaptive residual generator for the online identification of the fault diagnosis relevant vectors, and an iterative optimization method for system performance enhancement. The performance and effectiveness of the proposed schemes are demonstrated through the TE benchmark model.

A data-driven fault detection approach for static processes with deterministic disturbances

Abstract: Based on the well established model-based fault detection techniques, in this paper, a data-driven fault detection approach for static processes with deterministic disturbances is proposed. The basic idea behind this approach is, first identify the maximum influence of the unknown input on the measurement using the fault-free recorded data, and then apply the existing model-based schemes to solve the fault detection problem. The performance and effectiveness of the proposed scheme are demonstrated through a laboratory continuous stirred tank heater (CSTH) setup.

A data-based performance management framework for large-scale industrial processes

Abstract: A data-based framework is established for performance management in large-scale industrial processes. The main objective is to introduce the new framework and study the needed revisions, extensions and combined use of different methods under the proposed framework.