The key to ensuring rotating machinery’s safe and reliable operation is efficient and accurate faults diagnosis. Intelligent fault diagnosis technology based on deep learning (DL) has gained increasing attention. A critical challenge is how to embed the characteristics of time series into DL to obtain stable features that correlate with equipment conditions. This study proposes a lightweight rolling bearing fault diagnosis method based on Gramian angular field (GAF) and coordinated attention (CA) to improve rolling bearing recognition performance and diagnosis efficiency. Firstly, the time domain signal is encoded into GAF images after downsampling and segmentation. This method retains the temporal relation of the time series and provides valuable features for DL. Secondly, a lightweight convolution neural network (CNN) model is constructed through depthwise separable convolution, inverse residual block, and linear bottleneck layer to learn advanced features. After that, CA is employed to capture the long-range dependencies and identify the precise position information of the GAF images with nearly no additional computational overhead. The proposed method is tested and evaluated by CWRU bearing dataset and experimental dataset. The results demonstrate that the CNN based on GAF and CA (GAF-CA-CNN) model can effectively reduce the calculation overhead of the model and achieve high diagnostic accuracy.

In this study, a stable real-time monitoring system was established to monitor electrical and mechanical signals during resistance spot welding process. The sudden decrease of electrode voltage signal, the fluctuation of dynamic electrode force and the obvious decline of dynamic resistance could be used for recognizing expulsion phenomena during resistance spot welding which would reduce welding quality and should be avoided as much as possible. In order to research the welding quality estimation methods, four estimation models were built based on regression analysis and back-propagation neural network. The results showed that the estimation accuracy of back-propagation neural network was higher than the model of regression analysis, and the characteristic values of dynamic signals during resistance spot welding process could improve the estimation accuracy significantly.

The paper established a two-parameter Weibull distribution model to analyze the failure law of key components of metro vehicles and employed the least squares method to fit the parameters of the Weibull function. The paper provided a detailed introduction of the model's parameter fitting method based on actual historical failure data. Taking the EDCU (Electrical Door Control Unit) as an example, the failure rate, cumulative failure rate, and reliability of EDCU for 3 different vehicle models were calculated, and the calculation results were verified to conform to the Weibull distribution through Q-Q diagrams. The results show that although the designed service life of EDCU of vehicle door system is 15 years, the reliability of the EDCU decreases significantly around 10 years. The EDCU reliability of the three vehicle models declines by 22%, 4.2%, 10.6% and the failure rates are 2.7%, 0.57%, 1.13%. Based on the reliability of EDCU mentioned above, while ensuring the reliability of components, the maintenance methods of components can be optimized to reduce maintenance costs.

The microstructure, tensile properties and fracture behaviors of electron-beam welded Ti6242 titanium alloy joints were systematically investigated. The as-received alloy exhibited a typical duplex microstructure consisting of equiaxed primary α phase and laminated secondary α phase/β phase. The high cooling rate during welding resulted in substantially microstructure evolution in the fusion zone (FZ) of the joints, leading to the formation of large columnar β grains with embedded acicular α′ martensitic phase platelets. The influences of such microstructure variations on the mechanical properties and fracture behaviors at different temperatures were analyzed and discussed. In principle, the hard-yet-brittle α′ martensitic phase induced significantly strengthening of the FZ. As a result, for the un-notched specimens, the plastic deformation was concentrated to the soft base material with subsequent ductile fracture via micro-void coalescence mechanism. But for the notched specimen, in which the notch located just within the FR, cracks initiated and proceeded along the FR in a brittle manner.