An early fault detection method for induced draft fans based on MSET with informative memory matrix selection. (July 2020) - Lv, You; Fang, Fang; Yang, Tingting; Romero, Carlos E.
Abstract: 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. Highlights: MSET model with informative memory matrix is proposed to detect faults of ID fans. Samples are searched by DPSO to obtain informative memory matrix for model training. A similarity index is proposed based on the MSET model to produce a warning signal. MSET models trained by using memory matrices from other selections are compared. The obtained model produces a warning signal 170 min before an ID fan fault occurs.