and SSA-ELM

GU Wenjuan1, ZHANG Yang2

(1.Jiangxi Business School, Nanchang 330038, China; 

2.School of Mechanical Engineering, Hubei 

University of Technology, Wuhan 430068, China)

Abstract:  Aiming at the shortcomings of multi-scale permutation entropy in analyzing the vibration signal of centrifugal pump, which ignored signal amplitude information and had insufficient coarse granulation processing, resulting in low fault recognition accuracy, a fault diagnosis method of centrifugal pump based on improved multi-scale increment entropy (IMIE), multi cluster feature selection (MCFS) and sparrow search algorithm optimized extreme learning machine (SSA-ELM) was proposed. Firstly, based on improved coarse-grained processing, an improved multi-scale increment entropy (IMIE) method was proposed, which was used to extract the fault characteristics, and construct a characteristic matrix reflecting the damage properties of the centrifugal pump. Then, multi cluster feature selection (MCFS) was used to rank the importance of the original fault features, obtain fault features that contribute more to classification recognition,and improve the quality of fault features. Finally, the low-dimensional sensitive features were input into the extreme learning machine based on the sparrow search algorithm (SSA) for classification, and the identification of different fault types of centrifugal pumps was completed. The effectiveness of the proposed fault diagnosis method was experimentally tested by the fault data set of centrifugal pumps. The research results show that the fault identification accuracy of the proposed fault diagnosis method reaches 100%, and the average accuracy and standard deviation under multiple classification experiments are also better than other comparison fault diagnosis methods, which is superior to the comparative fault diagnosis method, that is, IMIE can accurately extract the fault information in the signal and characterize the health status of the centrifugal pump, and SSAELM can accurately identify the fault type of centrifugal pump, which proves that the method has certain effectiveness and advantages.

Key words: vane pump; improved coarsegrained processing; improved multiscale increment entropy(IMIE); multi cluster feature selection(MCFS); sparrow search algorithm(SSA); extreme learning machine(ELM); characteristic matrix