A Review of the Design and Feasibility of Intelligent Water-Lubrication Bearings

Water-lubrication bearings are critical components in ship operation.However,studies on their maintenance and failure detection are highly limited.The use of sensors to continually monitor the working operation of bearings is a potential approach to solve this problem,which is collectively called intelligent bearings.In this literature review,the recent progress of electrical resistance strain gauges,Fiber Bragg grating,triboelectric nanogenerators,piezoelectric nanogenerators,and thermoelectric sensors for in-situ monitoring is summarized.Future research and design concepts on intelligent water-lubrication bearings are also comprehensively discussed.The findings show that the accident risks,lubrication condition,and remaining life of water-lubricated bearings can be evaluated with the surface temperature,coefficient of friction,and wear volume monitoring.The research work on intelligent water-lubricated bearings is committed to promoting the development of green,electrified,and intelligent technologies for ship propulsion systems,which have important theoretical significance and application value.

Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks

Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during their use.However,because of the resource limitations of the end device,processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network(CNN),which involves a great amount of multiplicative operations.To minimize the computation cost of the conventional CNN,based on the idea of AdderNet,a 1-D adder neural network with a wide first-layer kernel(WAddNN)suitable for bearing fault diagnosis is proposed in this paper.The proposed method uses the l1-norm distance between filters and input features as the output response,thus making the whole network almost free of multiplicative operations.The whole model takes the original signal as the input,uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise,and then uses two layers of small kernels for nonlinear mapping.Through experimental comparison with CNN models of the same structure,WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost.The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.