Lv You, Fang Fang, Yang Tingting, Romero Carlos E
Key Laboratory of Power Station Energy Transfer Conversion and System, North China Electric Power University, Changping District, Beijing 102206, China; School of Control and Computer Engineering, North China Electric Power University, Changping District, Beijing 102206, China.
Key Laboratory of Power Station Energy Transfer Conversion and System, North China Electric Power University, Changping District, Beijing 102206, China; School of Control and Computer Engineering, North China Electric Power University, Changping District, Beijing 102206, China.
ISA Trans. 2020 Jul;102:325-334. doi: 10.1016/j.isatra.2020.02.018. Epub 2020 Feb 17.
Early fault detection of induced draft (ID) fans is very important to improve the reliability by providing predictive maintenance and reducing unscheduled shutdowns. This study proposed an early fault detection method for ID fans based on MSET with informative memory matrix selection. Firstly, to obtain an informative memory matrix, the discrete particle swarm optimization (DPSO) was utilized to search samples with large condition information. An accurate MSET model was then developed based on the memory matrix to produce predictions of the feature variables. Finally, a similarity index that represents the health status of the equipment was defined to give warnings of early faults. An application to detect the early faults of an ID fan in a coal-fired power plant was demonstrated to illustrate the effectiveness of proposed method.
