Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter | Zendy

Li Sun | Zendy; Junyi Dong | Zendy; Donghai Li | Zendy; Yuqiong Zhang | Zendy

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Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter

Author(s) -

Li Sun,

Junyi Dong,

Donghai Li,

Yuqiong Zhang

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/504086

Subject(s) - boiler (water heating) , combustion , fault detection and isolation , engineering , control theory (sociology) , heat transfer coefficient , heat transfer , computer science , mechanics , waste management , artificial intelligence , chemistry , physics , control (management) , organic chemistry , electrical engineering , actuator

Fluidized bed combustion (FBC) boilers have received increasing attention in recent decades. The erosion issue on the water wall is one of the most common and serious faults for FBC boilers. Unlike direct measurement of tube thickness used by ultrasonic methods, the wastage of water wall is reconsidered equally as the variation of the overall heat transfer coefficient in the furnace. In this paper, a model-based approach is presented to estimate internal states and heat transfer coefficient dually from the noisy measurable outputs. The estimated parameter is compared with the normal value. Then the modified Bayesian algorithm is adopted for fault detection and diagnosis (FDD). The simulation results demonstrate that the approach is feasible and effective

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