Bearings, as widely employed supporting components, frequently work in challenging working conditions, leading to diverse fault types. Traditional methods for diagnosing bearing faults primarily center on time–frequency analysis, but this often requires expert experience for accurate fault identification. Conversely, intelligent fault recognition and classification methods frequently lack interpretability. To address this challenge, this paper introduces a convolutional neural network with an attention mechanism method, denoted as CBAM-CNN, for bearing fault diagnosis. This approach incorporates an attention mechanism, creating a Convolutional Block Attention Module (CBAM), to enhance the fault feature extraction capability of the network in the time–frequency domain. In addition, the proposed method integrates a weight visualization module known as the Gradient-Weighted Class Activation Map (Grad-CAM), enhancing the interpretability of the convolutional neural network by generating visual heatmaps on fault time–frequency graphs. The experimental results demonstrate that utilizing the dataset employed in this study, the CBAM-CNN achieves an accuracy of 99.81%, outperforming the Base-CNN with enhanced convergence speed. Furthermore, the analysis of attention weights reveals that this method exhibits distinct focus of attention under various fault types and degrees. The interpretability experiments indicate that the CBAM module balances the weight allocation, emphasizing signal frequency distribution rather than amplitude distribution. Consequently, this mitigates the impact of the signal amplitude on the diagnostic model to some extent.

For the spindle of machine tool, rotation error is a main factor affecting the machining quality, so many investigations have been focused on the measurement of the rotation error. In this paper, a measurement and separation method of the rotation error is proposed to extract the orbit of spindle section centre and to quantize the rotation error with a special measurement system. Thereinto, systematic measurement strategy has been especially designed to achieve the measurement and analysis of the rotation error. When the measurement does not conform to the small error conditions, the method can reduce the noise level from sensors and improve measurement accuracy in measuring the orbit of spindle section centre. When it is necessary to accurately quantify the rotation error and the roundness error, the method also can effectively separate them with independent coordinate system and sensor bracket. Additionally, considering the effect of high temperature on spindle, the method can reduce the form error caused by thermal expansion in the measurement process, which is very important in industrial online measurement. Finally, the theoretical analyses are shown good agreement with experimental results, and these also reveal that it is an effective measurement method for the rotation error.

Wheel is an important component of bogie, and its geometry affects the dynamic behaviors of rail vehicles. To accurately obtain the geometric feature information of wheel tread profile, an on-machine measurement method with the 1-D laser sensor is investigated in this article. Combined with the specific morphology of tread profile and measurement model of laser sensor, the corresponding on-machine measurement model of wheel tread profile is established according to the characteristics of inclined and curved profiles. To improve the measurement accuracy, an adaptive error correction is studied to strengthen the measurement of complex profiles and suppress the measurement error of laser sensors. And the method achieves error suppression by more than 30% in the test and calibration experiments. Finally, the measurement method based on the laser sensor is put into the practical measurement of the wheel tread profile on an underfloor wheelset lathe, and the effectiveness of the proposed method is verified with experiments. These works can not only improve the measurement accuracy of wheel geometry but also enhance the machining capability of machine tool, which is important for the safe operation of rail vehicles.

The strain of the ring gear can reflect the dynamic characteristics of planetary gearboxes directly, which makes it an ideal signal to monitor the health condition of the gearbox. To overcome the disadvantages of traditional methods, a new approach for the dynamic measurement of ring gear strains using fiber Bragg gratings (FBGs) is proposed in this paper. Firstly, the installation of FBGs is determined according to the analysis for the strain distribution of the ring gear. Secondly, the parameters of the FBG are determined in consideration of the accuracy and sensitivity of the measurement as well as the size of the ring gear. The strain measured by the FBG is then simulated under non-uniform strain field conditions. Thirdly, a dynamic measurement system is built and tested. Finally, the strains of the ring gear are measured in a planetary gearbox under normal and faulty conditions. The experimental results showed good agreement with the theoretical results in values, trends, and the fault features can be seen from the time domain of the measured strain signal, which proves that the proposed method is feasible for the measurement of the ring gear strains of planetary gearboxes.

Out-of-round of wheel profile is a special phenomenon and an important factor affecting the high-speed running of rail vehicles. In this paper, based on the underfloor wheelset lathe, the on-machine measurement and evaluation method for the out-of-round of wheel profile is investigated to achieve the high-precision on-line machining and measuring of wheel profile. First of all, the on-machine measurement method and dynamic measurement model of the wheel profile are proposed, which are used to extract and separate the wheel profile from the measurement data to obtain the accurate profile data. Then, a new positioning solution of the wheel profile centre is recommended to obtain quickly the coordinates of the wheel profile centre, which helps to accurately calculate the diameter and roundness error of the measured wheel profile. For the evaluation of the out-of-round wheel, the order refinement is introduced to strengthen the physical resolution of the low-order polygon analysis, and it can further improve the evaluation accuracy of the wheel profile. Finally, with experimental examples, the results indicate that the proposed methods achieve excellent measurement behavior and can be put into the practical on-machine measurement of the wheel profile.