CF-HSACNN: A joint anti-noise learning framework for centrifugal fan state recognition

Abstract

State recognition of centrifugal fans is a challenging task. Because the difference of the fault signals collected is small in a noisy environment, it is difficult to capture useful features, which leads to a decrease in diagnostic efficiency. With the purpose of solving this problem, paper proposes a method that complete-spectrum fast entropy discrimination matches hybrid self-attention convolutional neural network (CF-HASCNN). The method uses fixed-point iteration to perform signal reconstruction on the vector of the complete spectrum decomposition, and uses fuzzy entropy threshold discrimination to weaken the interference of noise on the signal. Then, the HASCNN captures subtle useful information in two aspects: space and single point. In addition, the operating rules of the model are studied to provide an explanation basis. The method which has proposed has stronger anti-interference ability, compared with other advanced diagnostic models.