Rolling bearing fault diagnosis based on bacterial foraging algorithm and deep belief network

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (23) : 68-74.

Tao Jie1,2Liu Yilun1,3Yang Dalian1,4Bin Guangfu4

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Abstract

When studying rolling bearing fault diagnosis with the deep belief network method, parameters in the deep belief network have a great effect on fault diagnosis results and it is hard to obtain suitable parameters. Here, the fault diagnosis method based on the bacterial foraging algorithm and the deep belief network was proposed to improve the correct rate of bearing fault diagnosis. The parallel search ability of the bacterial foraging algorithm was adopted to effectively choose the number of hidden layer, the number of hidden nodes, the learning rate in a deep belief network. The deep belief network’s training data classification error was used to calculate the fitness function of the bacterial foraging algorithm to build an appropriate fault classifier and finish rolling bearing fault diagnosis. The test results showed that the correct rate of the proposed method for rolling bearing fault diagnosis reaches 98.5%; compared with BPNN, SVM and KNN methods, the proposed method can more stably and more accurately identify rolling bearing faults.

Key words

deep belief network / bacterial foraging algorithm / rolling bearings / fault diagnosis

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Tao Jie1,2Liu Yilun1,3Yang Dalian1,4Bin Guangfu4. Rolling bearing fault diagnosis based on bacterial foraging algorithm and deep belief network[J]. Journal of Vibration and Shock, 2017, 36(23): 68-74

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