The health status of industrial equipment is related to the normal operation of industrial production.Therefore,a method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and bidirectional long-term short-term memory network (Bi-LSTM) method was proposed for predicting the health status of industrial equipment.ICEEMDAN was used to decompose the original audio signal to obtain several intrinsic mode function (IMF) components,the best component group was selected by calculating the correlation coefficient for signal reconstruction,and then the fuzzy entropy structure of the reconstructed IMF component was calculated to reconstruct the feature vector set.The set of feature vectors was finally input to the Bi-LSTM network for model training and prediction.The experimental results show that,compared with other models,the health status prediction method of industrial equipment based on ICEEMDAN fuzzy entropy and Bi-LSTM can effectively extract the audio signal features and accurately predict the health status.