With the rapid development of precision instruments, aerospace, and automotive industries, the demand for compact vibration isolators capable of suppressing low-frequency vibrations has surged. Although prior reviews have established the theoretical framework of quasi-zero stiffness (QZS) isolators, critical gaps persist in addressing their compact design under strong nonlinear dynamics and diverse engineering constraints. This review systematically analyzes the dynamic characteristics of QZS systems under nonlinear effects and evaluates five innovative design methodologies for compact QZS isolators: special spring type, magnetic type, bionic type, metamaterials-based type, and origami-inspired type. Key findings reveal that special spring-type isolators are simple to design and space-efficient but difficult to machine. Magnetic-type isolators achieve ultra-low start-up frequencies but face thermal instability. Metamaterial designs enable multifunctional integration at the cost of manufacturing complexity, while bionic-inspired and origami-inspired isolators are difficult to abstract for practical applications. We find that current research tends to prioritize miniaturization over the synergistic optimization of load capacity, broadband isolation, and adaptability. Future research should focus on multi-degree-of-freedom systems, coupled metamaterials-bionic structures, and active magnetic control. This work provides a key roadmap for advancing compact QZS technology in space-constrained applications.

As the most basic parts of rotating machinery, the health condition of rolling bearing affects the performance and safety of the whole mechanical equipment. Therefore, the accurate fault diagnosis of rolling bearings is the basic condition to ensure the good working condition of the equipment. This passage reviews the research on fault diagnosis methods at home and abroad. Based on two aspects of fault feature extraction and fault pattern recognition, the advantages and disadvantages of the main methods were analyzed respectively, and the research trend of bearing fault diagnosis was summarized and prospected.

The leakage of the ship’s pipeline system will bring great risks to the engine equipment and seriously threaten the vitality of the ship. In this paper, the pipeline leakage detection and localization research are carried out based on the vibration signal generated by pipeline leakage. First, the finite element model of the pipeline is constructed to obtain the variation law of the vibration signal when the pipeline leaks are carried out. Second, the vibration signal is processed based on the variational mode decomposition (VMD) and radial basis function (RBF) neural networks. The wavelet packet threshold noise reduction is conducted before signal decomposition to improve the signal-to-noise ratio. Then, the denoised signal is decomposed by VMD. The effective component is identified by analyzing the correlation coefficient between the component and the denoised signal. The center frequency and energy of the effective component are used as feature vector to train the RBF neural network to identify and locate leakage. Finally, a pipeline leakage test platform is built under laboratory conditions. After processing the data samples collected from the test, the RBF neural network is trained to identify and locate leaks. The test sample identification results show that the leak identification and localization method based on VMD-RBF has a high accuracy.

Weak fault signal not only refers to the signal with very small amplitude, but also refers to the signal existing in the background of strong noise. Weak signal detection technology is widely used in communication, radar, magnetism, vibration measurement, medicine and other research fields. Based on the essence of chaos, this paper summarizes the research status of chaos at home and abroad, including the basic characteristics of chaos system, the basic characteristics of chaos, the chaotic dynamics model and the discriminant method of chaos. The different methods of chaotic weak fault feature extraction are analyzed, their advantages and disadvantages are pointed out, and the research trend is forecasted