Remaining useful life prediction is one of the essential processes for machine system prognostics and health management. Although there are many new approaches based on deep learning for remaining useful life prediction emerging in recent years, these methods still have the following weaknesses: (1) The correlation between the information collected by each sensor and the remaining useful life of the machinery is not sufficiently considered. (2) The accuracy of deep learning algorithms for remaining useful life prediction is low due to the high noise, over-dimensionality, and non-linear signals generated during the operation of complex systems. To overcome the above weaknesses, a general deep long short memory network-based approach for mechanical remaining useful life prediction is proposed in this paper. Firstly, a two-step maximum information coefficient method was built to calculate the correlation between the sensor data and the remaining useful life. Secondly, the kernel principal component analysis with a simple moving average method was designed to eliminate noise, reduce dimensionality, and extract nonlinear features. Finally, a deep long short memory network-based deep learning method is presented to predict remaining useful life. The efficiency of the proposed method for remaining useful life prediction of a nonlinear degradation process is demonstrated by a test case of NASA’s commercial modular aero-propulsion system simulation data. The experimental results also show that the proposed method has better prediction accuracy than other state-of-the-art methods.

As an important part of head protection equipment, research on the material and structural application of helmet liners has always been one of the hotspots in the field of helmets. This paper first discusses common helmet liner materials, including traditional polystyrene, polyethylene, polypropylene, etc., as well as newly emerging anisotropic materials, polymer nanocomposites, etc. Secondly, the design concept of the helmet liner structure is discussed, including the use of a multi-layer structure, the addition of geometric irregular bubbles to enhance the energy absorption effect, and the introduction of new manufacturing processes, such as additive manufacturing technology, to realize the preparation of complex structures. Then, the application of biomimetic structures to helmet liner design is analyzed, such as the design of helmet liner structures with more energy absorption properties based on biological tissue structures. On this basis, we propose extending the concept of bionic structural design to the fusion of plant stalks and animal skeletal structures, and combining additive manufacturing technology to significantly reduce energy loss during elastic yield energy absorption, thus developing a reusable helmet that provides a research direction for future helmet liner materials and structural applications.

A high-precision wear particle monitoring instrument based on numerical simulation, machine vision, multi-target tracking algorithm, and microfluidic technology is designed and developed in this paper. The instrument not only performs the traditional particle number and size detection, but also proposes a particle density detection model based on motion velocity. Firstly, an on-line visual microfluidic chip for oil particles motion was specially designed. Then a mathematical model relationship between particles velocity and particles diameter, particles density, oil viscosity, oil density was established by force analysis and solving the equation, which was used to explain the working principle of the instrument. Meanwhile, a multi-target tracking algorithm incorporating the characteristics of wear particles based on YOLOv5-Deepsort was custom-developed to detect the number, size, and motion characteristics of particles under the optical field of view of the image sensor. Secondly, the oil flow field distribution and the four factors affecting the particle motion characteristics in the self-developed chamber, particle size, particle material, particle radial position, and oil velocity were analyzed by using the finite element analysis in COMSOL. Finally, a pin-disk wear experimental validation results demonstrated this approach has excellent suitability to monitor the number, size, and density of wear particles with diameters above 20μm and densities above 2000 kg/m3 simultaneously. This portable oil particle monitoring instrument provides technical support for condition monitoring, fault diagnosis, and intelligent maintenance of rotating mechanical equipment.