Abstract
Due to the problem of poor recognition of data with deep fault attribute in the case of traditional superficial network under semisupervised and weak labeling, a deep belief network (DBN) was proposed for deep fault detection. Due to the problems of deep belief network (DBN) network structure and training parameter selection, a stochastic adaptive particle swarm optimization (RSAPSO) algorithm was proposed in this study to optimize the DBN. A stochastic criterion was proposed in this method to make the particles jump out of the original position search with a certain probability and reduce the probability of falling into the local optimum. The RSAPSO-DBN method used sample data to train the DBN and used the final diagnostic error rate to construct the fitness value function of the particle swarm algorithm. By comparing the minimum fitness value of each particle to determine the advantages and disadvantages of the model, the corresponding minimum fitness value was selected. Using the number of network nodes, learning rate, and momentum parameters, the optimal DBN classifier was generated for fault diagnosis. Finally, the validity of the method was verified by bearing data from Case Western Reserve University in the United States and data collected in the laboratory. Comparing BP (BP neural network), support vector machine, and heterogeneous particle swarm optimization DBN methods, the proposed method demonstrated the highest recognition rates of 87.75% and 93.75%. This proves that the proposed method possesses universality in fault diagnosis and provides new ideas for data identification with different fault depth attributes.
Highlights
Machine learning is a popular research interest in artificial intelligence and pattern recognition
The main intelligent diagnostic methods include support vector machine (SVM), artificial neural network (ANN), multilayer perceptron (MLP), kernel method (KMs), and other pattern recognition methods [5,6,7,8]. ese methods have achieved desirable results in the fault diagnosis of mechanical equipment, but they belong to the algorithm structure called “shallow learning.”
Using the parallel search capability of the RSAPSO, the model parameters of deep belief network (DBN) were optimized and selected. is method used DBN training on the sample data to construct a fitness function. e final recognition error rate was used as the termination condition of the improved particle swarm algorithm iteration. e RSAPSO is associated with the parameter optimization of the DBN to effectively generate a suitable classifier to improve the accuracy of fault diagnosis rate
Summary
Machine learning is a popular research interest in artificial intelligence and pattern recognition. Ese methods have achieved desirable results in the fault diagnosis of mechanical equipment, but they belong to the algorithm structure called “shallow learning.”. Shao et al [17] proposed a DBN for time-domain feature extraction and particle swarm optimization (PSO) for the fault diagnosis of rolling bearings. These studies have only set DBN’s structural parameters based on experience or repeated experiments. Erefore, to improve the accuracy of fault diagnosis and reduce the optimization time of the model, this study proposes a fault diagnosis method based on the stochastic adaptive particle swarm algorithm (RSAPSO) and DBN. Using the parallel search capability of the RSAPSO, the model parameters of DBN were optimized and selected. is method used DBN training on the sample data to construct a fitness function. e final recognition error rate was used as the termination condition of the improved particle swarm algorithm iteration. e RSAPSO is associated with the parameter optimization of the DBN to effectively generate a suitable classifier to improve the accuracy of fault diagnosis rate
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