As a fundamental element in industrial fluid transportation, the main pump fulfills an irreplaceable function in critical infrastructure, including the energy, water conservancy, petrochemical, and sewage treatment industries. As the core component of key power equipment, its operating condition is intrinsically connected to the safety, stability, and reliability of the entire system. This paper provides a systematic review of the latest advances in fault mechanism analysis and diagnosis methods for main pump systems. First, the typical structural composition and functional characteristics of the main pump system are examined, and the occurrence mechanisms and evolution rules of typical faults, such as mechanical malfunctions and performance degradation caused by hydraulic imbalance, are discussed in detail. Second, the main technical approaches to fault diagnosis are summarized and reviewed, including diagnosis methods based on signal processing, modeling, data-driven techniques, and multi-source information fusion. The advantages, limitations, and application scopes of these approaches are comparatively analyzed. On this basis, the development trends in main pump fault diagnosis technology and the key challenges faced—such as strong noise, small sample size, and multiple fault coupling—are identified and discussed. Finally, future research prospects are put forward in view of the limitations of current research. This review aims to provide theoretical insights and technical support for advancing condition monitoring, fault diagnosis, and health management of main pump systems.

Flexible pressure sensors have attracted great attention due to their extensive applications in human–computer interaction and health monitoring. So far, the development of flexible pressure sensors that balance high sensitivity and a wide measurement range remains a challenge. Herein, a double-layer dielectric structure with a surface convex structure is reported for the preparation of flexible capacitive pressure sensors. The double-layer dielectric structure, which is composed of a silicone rubber-based conductive elastomer with a surface micro-convex structure and a PVA-H-based conductive elastomer, balances the advantages and disadvantages of the two conductive elastomer dielectrics. It can form a complete micro-capacitive network under relatively large pressures, enabling the sensor to have high sensitivity at different stages (1.7 kPa⁻¹, 0–104 kPa; 19.14 kPa⁻¹, 104–140 kPa), thus achieving a dual enhancement of sensitivity and sensing range. Additionally, the sensor has been successfully applied to scenarios such as monitoring of human breathing, speaking, and movement, as well as mouse clicks, demonstrating its great potential in the fields of health monitoring and human–computer interaction applications.

As one of the typical propulsion systems of the new-energy vehicles (NEVs), the dual-motor driving system (DMDS) is installed and fixed on the bodywork and moves together with the bodywork in space, that is, the DMDS works in non-inertial systems. The previous researches on the DMDS are based on the assumption that the bodywork is stationary. In fact, since the DMDS moves with the bodywork, besides its own excitation forces, it is inevitably affected by the additional inertial terms (AITs) caused by the change in the operating state of the NEVs. In order to investigate the dynamic response and synchronizing characteristic of the DMDS under different motion forms of NEVs, the dynamic model of the DMDS in a non-inertial system is established, considering the permanent magnet synchronous motors (PMSMs), time-varying meshing stiffness and transmission error of gears. Subsequently, the expressions of the AITs are deduced under different non-inertial conditions. The translational motion and circular motion of the vehicle are selected to analyze the dynamic response and synchronizing characteristics of the DMDS in a non-inertial system. The results show that the acceleration has a significant influence on the displacement response of gears, torque and speed of PMSMs, but the torque synchronization and speed synchronization between PMSMs are minimally influenced by both the acceleration and the AITs. Meanwhile, the AITs that affect the displacement response are analyzed and quantified.

Flexible tactile sensors have attracted significant interest in robotics, medical monitoring, and wearable devices. This paper presents a capacitive flexible tactile sensor that employs a nickel carbonyl powder (NCP)-silicone rubber (SR) composite for pressure and bending sensing, fabricated using magnetic field curing. The performance of the sensor is evaluated independently for pressure and bending sensing, including sensitivity, response time, repeatability, and cyclic stability. The pressure sensor exhibits a notable capacitance changing of 171.9% within the range of 0–41 KPa, demonstrating fast (80 ms) responses and (120 ms) recovery times, and indicates excellent repeatability under different peak pressure ranging from 2–15 KPa. It also shows excellent stability across 5000 repeated compression tests. Meanwhile, the bending sensor reveals a significant capacitance changing of 108% at ranging from 0°–180°, with rapid (80 ms) responses and (48 ms) recovery times, and indicates stable repeatability under different peak angle ranging from 60°–150°. Its reliable cyclic stability is confirmed through 1000 bending cyclic tests. Furthermore, the microscopic properties of magnetorheological elastomers (MRE) are further elucidated. Notably, the sensor is integrated into a breathable, lightweight glove, facilitating feedback on grasping pressure and enabling sign language recognition (SLR). When paired with machine learning (ML), it achieves an impressive accuracy of 97.73% in SLR. Overall, this research advances the development of flexible tactile sensors and highlights their considerable potential in human–computer interaction, medical health, wearable devices.