•Gear mesh stiffness evaluation methods are reviewed.•Gearbox damage modeling and fault diagnosis techniques are reviewed.•Gearbox transmission path modeling is reviewed.•Validation techniques are ...reviewed.•Research prospects are suggested.
Gearbox is widely used in industrial and military applications. Due to high service load, harsh operating conditions or inevitable fatigue, faults may develop in gears. If the gear faults cannot be detected early, the health will continue to degrade, perhaps causing heavy economic loss or even catastrophe. Early fault detection and diagnosis allows properly scheduled shutdowns to prevent catastrophic failure and consequently result in a safer operation and higher cost reduction. Recently, many studies have been done to develop gearbox dynamic models with faults aiming to understand gear fault generation mechanism and then develop effective fault detection and diagnosis methods. This paper focuses on dynamics based gearbox fault modeling, detection and diagnosis. State-of-art and challenges are reviewed and discussed. This detailed literature review limits research results to the following fundamental yet key aspects: gear mesh stiffness evaluation, gearbox damage modeling and fault diagnosis techniques, gearbox transmission path modeling and method validation. In the end, a summary and some research prospects are presented.
For the data-driven remaining useful life (RUL) prediction for rolling bearings, the traditional machine learning-based methods generally provide insufficient feature representation and adaptive ...extraction. Although deep learning-based RUL prediction methods can solve these problems to some extent, they still do not yield satisfactory predictive results due to less degradation data and inconsistent data distribution among different bearings. To solve these problems, a new RUL prediction method based on deep feature representation and transfer learning is proposed in this paper. This method includes an off-line stage and an online stage. In the off-line stage, the Hilbert-Huang transform marginal spectra of the raw vibration signal of auxiliary bearings are first calculated as the input, and then contractive denoising autoencoder is introduced to extract deep features with good and stable fault representation. Second, by using the obtained deep features and Pearson's correlation coefficient, a new health condition assessment method is proposed to divide the whole life of each bearing into a normal state and a fast-degradation state. Finally, using the extracted deep features and their RUL values, an RUL prediction model for the fast-degradation state is trained by means of a least-square support vector machine. In the online stage, a kind of transfer learning algorithm, i.e., transfer component analysis, is introduced to sequentially adjust the features of target bearing from auxiliary bearings, and then the corresponding RUL is predicted using the corrected features. Results using the PHM Challenging 2012 data set show a significant performance improvement when using the proposed method in terms of predictive accuracy and numerical stability.
Efforts to extend nanoparticle residence time in vivo have inspired many strategies in particle surface modifications to bypass macrophage uptake and systemic clearance. Here we report a top-down ...biomimetic approach in particle functionalization by coating biodegradable polymeric nanoparticles with natural erythrocyte membranes, including both membrane lipids and associated membrane proteins for long-circulating cargo delivery. The structure, size and surface zeta potential, and protein contents of the erythrocyte membrane-coated nanoparticles were verified using transmission electron microscopy, dynamic light scattering, and gel electrophoresis, respectively. Mice injections with fluorophore-loaded nanoparticles revealed superior circulation half-life by the erythrocyte-mimicking nanoparticles as compared to control particles coated with the state-of-the-art synthetic stealth materials. Biodistribution study revealed significant particle retention in the blood 72 h following the particle injection. The translocation of natural cellular membranes, their associated proteins, and the corresponding functionalities to the surface of synthetic particles represents a unique approach in nanoparticle functionalization.
Gold nanoparticles are enclosed in cellular membranes derived from natural red blood cells (RBCs) by a top‐down approach. The gold nanoparticles exhibit a complete membrane surface layer and ...biological characteristics of the source cells. The combination of inorganic gold nanoparticles with biological membranes is a compelling way to develop biomimetic gold nanostructures for future applications, such as those requiring evasion of the immune system.
Development of functional nanoparticles can be encumbered by unanticipated material properties and biological events, which can affect nanoparticle effectiveness in complex, physiologically relevant ...systems. Despite the advances in bottom-up nanoengineering and surface chemistry, reductionist functionalization approaches remain inadequate in replicating the complex interfaces present in nature and cannot avoid exposure of foreign materials. Here we report on the preparation of polymeric nanoparticles enclosed in the plasma membrane of human platelets, which are a unique population of cellular fragments that adhere to a variety of disease-relevant substrates. The resulting nanoparticles possess a right-side-out unilamellar membrane coating functionalized with immunomodulatory and adhesion antigens associated with platelets. Compared to uncoated particles, the platelet membrane-cloaked nanoparticles have reduced cellular uptake by macrophage-like cells and lack particle-induced complement activation in autologous human plasma. The cloaked nanoparticles also display platelet-mimicking properties such as selective adhesion to damaged human and rodent vasculatures as well as enhanced binding to platelet-adhering pathogens. In an experimental rat model of coronary restenosis and a mouse model of systemic bacterial infection, docetaxel and vancomycin, respectively, show enhanced therapeutic efficacy when delivered by the platelet-mimetic nanoparticles. The multifaceted biointerfacing enabled by the platelet membrane cloaking method provides a new approach in developing functional nanoparticles for disease-targeted delivery.
Cell-derived nanoparticles have been garnering increased attention due to their ability to mimic many of the natural properties displayed by their source cells. This top-down engineering approach can ...be applied toward the development of novel therapeutic strategies owing to the unique interactions enabled through the retention of complex antigenic information. Herein, we report on the biological functionalization of polymeric nanoparticles with a layer of membrane coating derived from cancer cells. The resulting core–shell nanostructures, which carry the full array of cancer cell membrane antigens, offer a robust platform with applicability toward multiple modes of anticancer therapy. We demonstrate that by coupling the particles with an immunological adjuvant, the resulting formulation can be used to promote a tumor-specific immune response for use in vaccine applications. Moreover, we show that by taking advantage of the inherent homotypic binding phenomenon frequently observed among tumor cells the membrane functionalization allows for a unique cancer targeting strategy that can be utilized for drug delivery applications.
Synthetic nanoparticles coated with cellular membranes have been increasingly explored to harness natural cell functions toward the development of novel therapeutic strategies. Herein, we report on a ...unique bacterial membrane-coated nanoparticle system as a new and exciting antibacterial vaccine. Using Escherichia coli as a model pathogen, we collect bacterial outer membrane vesicles (OMVs) and successfully coat them onto small gold nanoparticles (AuNPs) with a diameter of 30 nm. The resulting bacterial membrane-coated AuNPs (BM-AuNPs) show markedly enhanced stability in biological buffer solutions. When injected subcutaneously, the BM-AuNPs induce rapid activation and maturation of dendritic cells in the lymph nodes of the vaccinated mice. In addition, vaccination with BM-AuNPs generates antibody responses that are durable and of higher avidity than those elicited by OMVs only. The BM-AuNPs also induce an elevated production of interferon gamma (INFγ) and interleukin-17 (IL-17), but not interleukin-4 (IL-4), indicating its capability of generating strong Th1 and Th17 biased cell responses against the source bacteria. These observed results demonstrate that using natural bacterial membranes to coat synthetic nanoparticles holds great promise for designing effective antibacterial vaccines.
A thorough understanding of the spectral structure of planetary gear system vibration signals is helpful to fault diagnosis of planetary gearboxes. Considering both the amplitude modulation and the ...frequency modulation effects due to gear damage and periodically time variant working condition, as well as the effect of vibration transfer path, signal models of gear damage for fault diagnosis of planetary gearboxes are given and the spectral characteristics are summarized in closed form. Meanwhile, explicit equations for calculating the characteristic frequency of local and distributed gear fault are deduced. The theoretical derivations are validated using both experimental and industrial signals. According to the theoretical basis derived, manually created local gear damage of different levels and naturally developed gear damage in a planetary gearbox can be detected and located.
•A new adaptive sequential sampling method is proposed for efficient structural reliability analysis.•Three learning functions are developed for selecting the most suitable sample point at each ...iteration.•Two stopping criterions are given to terminate the proposed adaptive sequential sampling algorithm.•The proposed method can be used, in principle, in any existing surrogate models.
Surrogate models are often used to alleviate the computational burden for structural systems with expensively time-consuming simulations. In this paper, a new adaptive surrogate model based efficient reliability method is proposed to address the issues that many existing adaptive sequential sampling reliability methods are limited to the Kriging models and Krging model-based Monte Carlo simulation (MCS) reliability methods produce random results even without considering the uncertainty from initial samples. Three learning functions are developed for selecting the most suitable training sample points at each iteration, and the learning functions ψσ and ψm are generally suggested because they were found to perform a bit better in most of the cases. Furthermore, most of the newly selected training sample points are ensured to reside far away from existing sample points and reside as close to the limit-state functions as possible. Two stopping criterions are given to terminate the proposed adaptive sequential sampling algorithm. The main advantages of the proposed method are that it not only provides an efficient manner for structural reliability analysis with multiple failure modes to produce a determined result under without considering the uncertainty from initial samples, but also can be used, in principle, in any existing surrogate models. The accuracy and efficiency as well as applicability of the proposed method are demonstrated using three numerical examples.
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