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A self-adaptive deep belief network with Nesterov momentum for the fault diagnosis of rolling element bearings

Effective fault diagnosis of rotating machinery helps prevent unexpected machine breakdowns resulting from the failure of essential components. Traditional artificial intelligence methods, such as artificial neural networks and support vector machine, have been proved to be effective in fault identification. However, extracting features manually requires a high degree of expertise in signal processing. Deep belief network (DBN) has gained popularity as a new method for machine learning because of its potential merits such as its capability to extract effective features automatically in fault diagnosis. Therefore, a novel adaptive learning rate DBN with Nesterov momentum is proposed in this study for the fault diagnosis of rolling element bearings. An experiment is conducted using a dataset of bearing health states obtained from a test rig to substantiate the utility of the proposed DBN architecture. Results show that the proposed method demonstrates impressive performance in fault pattern recognition. Comparison analyses are further conducted to demonstrate that the advanced method performs better than current methods.