To implement Prognostics Health Management (PHM) for hydraulic pumps, it is very important to study the faults of hydraulic pumps to ensure the stability and reliability of the whole life cycle. The research on fault diagnosis has been very active, but there is a lack of systematic analysis and summary of the developed methods. To make up for this gap, this paper systematically summarizes the relevant methods from the two aspects of fault diagnosis and health management. In addition, in order to further facilitate researchers and practitioners, statistical and comparative analysis of the reviewed methods is carried out, and a future development direction is prospected.

The precision and stability of seam position detection are critical for single-square-groove weld seams formed using two thin metal plates. However, traditional methods, such as structured laser light imaging, struggle with narrow seams that lack misalignment and have high reflectivity, while non-structured light approaches are prone to welding light interference and speckle noise. To overcome these challenges, we propose a versatile optical design that leverages differential illumination to generate differential phase contrast (DPC) images. By processing images captured under differential illumination, the DPC method notably enhances seam edge contrast and suppresses welding light noise, improving the detection robustness and reliability. This approach provides a promising solution for high-precision weld seam detection in challenging environments.

As an important part of transportation, bridges play an important role in the national economy. As the number of bridges continues to increase, their maintenance and repair tasks continue to increase. In order to solve the problem of limited application scenarios and high cost of existing bridge maintenance vehicles, this paper proposes a surface crack recognition and repair system for small and medium-sized bridges, which is low in efficiency and affects traffic. It uses survey UAVs to collect bridge surface defect information, uses image processing technology to identify cracks, and the maintenance vehicle automatically navigates to repair the bridge deck crack at the location of the crack.

To address the issue of evaluating learning quality in virtual assembly teaching systems, an evaluation program that combines Petri nets and the fuzzy comprehensive evaluation method has been designed. First, a systematic learning quality evaluation indicator system is established by integrating the operational mode of the virtual assembly teaching system. Next, a Petri net model of the equipment assembly process is created by analyzing the real assembly process of the equipment. Based on this model, a quantitative scheme for the relevant evaluation indicators is formulated. Subsequently, a course-oriented learning quality evaluation model is developed using the fuzzy comprehensive evaluation method. Finally, a student is used as an evaluation object for example analysis. The constructed fuzzy comprehensive evaluation model processes the data to obtain the student’s overall rating. Practice has shown that this model can effectively assess the quality of student learning, achieving an organic combination of qualitative judgment and quantitative analysis, and providing a reference for evaluating student learning quality.