Non-contact diagnosis for gearbox based on the fusion of multi-sensor heterogeneous data

Non-contact sensing technology plays an important role in the health monitoring of the gearbox. However, a single non-contact measurement is challenging to achieve the simultaneous monitoring of both structural and non-structural damages. In order to explore the fusion mechanism of multi-sensor heterogeneous measurements, acoustic and thermal characteristics of the gearbox under typical fault states are analyzed, and it is verified the fusion of infrared thermal (IRT) images and acoustic data integrates complementary fault information. In this paper, an attention-enhanced information fusion diagnosis network (AIFN-IA) is proposed for the complementary fusion of IRT images and acoustic data. Firstly, the acoustic data is converted into images by the non-hyperparameter encoding method and then fused with IRT images in data-level. Secondly, the limited shuffle attention module is designed to adaptively focus on the fault elements hidden in the complex fusion features. Finally, experimental data verify the effectiveness of the proposed AIFN-IA method in recognizing six structural and non-structural damages of the gearbox. Compared with seven state-of-the-art methods, the proposed AIFN-IA method performs best in extracting discriminating features with the highest diagnosis accuracy. Moreover, the proposed AIFN-IA method can still achieve satisfactory results under the challenges of small sample datasets and strong noise interference, which is more competitive in real industrial applications.

Automated summary

Non-contact sensing technology is important for health monitoring of gearboxes, but it is challenging to simultaneously monitor both structural and non-structural damages with a single measurement method. This paper proposes an attention-enhanced information fusion diagnosis network (AIFN-IA) for the complementary fusion of infrared thermal (IRT) images and acoustic data. The experimental results verify the effectiveness of the proposed method in recognizing various damages in gearboxes and show that it outperforms state-of-the-art methods in terms of diagnosis accuracy, even with limited samples and noise interference.