Coupled neurons with multi-objective optimization benefit incipient fault identification of machinery

Organisms can sense subtle changes in the environment around them such as temperature, vibration and magnetic field. That is because biological neural network interconnected millions of neurons by synapses is able to utilize noise to amplify such subtle changes, and then encode and transmit them to make the corresponding biological responses. Such favorable use of noise can be improved by coupling two different neurons like synapses in organisms to enhance weak useful signature identification. Inspired by above mechanism, we investigate the benefits of noise in the coupled neurons to weak useful signature identification and then propose an adaptive coupled neurons-based method with multi-objective optimization to enhance incipient fault signature identification of machinery for overcoming the drawback that blind noise suppression and cancellation using unwanted noise techniques not only are prone to remove weak useful signature closely related with health states of machinery but also are impossible to cancel strong background noise. In the proposed method, both signal-to-noise ratio (SNR) and residence-time distribution ratio are seen as the multi-objective function to optimize the adjusting parameters of the coupled neurons and rescaling factor simultaneously by using genetic algorithms. Finally, two rolling element bearing experiments including a double row bearing run-to-failure experiment and a high-speed train bearing experiment were performed to demonstrate the feasibility and effectiveness of the proposed method in mechanical incipient fault diagnosis. The experimental results show that the proposed method not only enhances weak fault signature identification of machinery by coupling two different neurons but also is superior to the filter-based methods.