Independent component analysis approach for fault diagnosis of condenser system in thermal power plant
来源期刊：中南大学学报(英文版)2014年第1期
论文作者：Ajami Ali Daneshvar Mahdi
文章页码：242 - 251
Key words：condenser; fault detection and diagnosis; independent component analysis; independent component analysis (ICA); principal component analysis (PCA); thermal power plant
Abstract: A statistical signal processing technique was proposed and verified as independent component analysis (ICA) for fault detection and diagnosis of industrial systems without exact and detailed model. Actually, the aim is to utilize system as a black box. The system studied is condenser system of one of MAPNA’s power plants. At first, principal component analysis (PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones. Then, the fault sources were diagnosed by ICA technique. The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states, and it can distinguish main factors of abnormality among many diverse parts of a power plant’s condenser system. This selectivity problem is left unsolved in many plants, because the main factors often become unnoticed by fault expansion through other parts of the plants.
Ajami Ali1, Daneshvar Mahdi2
(1. School of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran, 5375171379;
2. MAPNA Electrical and Control Engineering and Manufacturing Company (MECO), Tehran, Iran, 31676-43111)
Abstract:A statistical signal processing technique was proposed and verified as independent component analysis (ICA) for fault detection and diagnosis of industrial systems without exact and detailed model. Actually, the aim is to utilize system as a black box. The system studied is condenser system of one of MAPNA’s power plants. At first, principal component analysis (PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones. Then, the fault sources were diagnosed by ICA technique. The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states, and it can distinguish main factors of abnormality among many diverse parts of a power plant’s condenser system. This selectivity problem is left unsolved in many plants, because the main factors often become unnoticed by fault expansion through other parts of the plants.
Key words:condenser; fault detection and diagnosis; independent component analysis; independent component analysis (ICA); principal component analysis (PCA); thermal power plant
