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Fault Monitoring Method Based on Principal Component Analysis and Its Application

Author: LiuYuHang
Tutor: QianFeng; LiuYuanQuan
School: East China University of Science and Technology
Course: Control Engineering
Keywords: fault diagnosis TE process cracked gas compressor residual analysis periodicdisturbance ICA
CLC: TP274
Type: Master's thesis
Year: 2012
Downloads: 219
Quote: 0
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Abstract


In process industry, the key to ensure safety is the effective process monitoring. PCA and ICA method are the usual data-based process monitoring methods. Principal Component Analysis (PCA) technology can extract some independent components from high dimensional data space and project them to an independent low dimensional data space. At the same time, most of the process information can be reserved. So it greatly decreases the difficulty to analyze multi-dimensional process variability and can be widely used in process monitoring.Independent Component Analysis (ICA) searches for the underlying statistical independent component in the data and explores the inherent characteristics. The theory shows its broad application prospects and powerful vitality. Due to non-Gaussian problem with periodic disturbance in the industrial process, an improved independent component analysis method based on residual analysis is proposed. Firstly, the algorithm eliminates periodic oscillation in data through processing residual between sample data and actual data. Then the independent component information of residual is extracted using ICA method. The simulations to Tennessee Eastman (TE) process and the cracked gas compressor verify that the algorithm is effective and has better fault monitoring capability than previous methods.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation systems > Data processing, data processing system
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