A new shaft multi-objective optimization dynamic balancing method based on differential search algorithm

Combined with the influence coefficient methods and the Holo-balancing theory of shaft system, a new shaft multi-objective optimization dynamic balancing method including energy, uniformity and maximum of the residual vibration is proposed by building a multi-objective fuzzy evaluation function and application of Differential Search (DS) algorithm. The advantage of DS algorithm is studied by comparing with four other optimization algorithms. And the principle of balancing weight optimization of DS algorithm is studied to realize the shaft dynamic balancing. Finally, the validity and effectiveness of the proposed method is verified through a field power generator set balancing case

A new time synchronous average method for variable speed operating condition gearbox

Gearbox is a widely used component for power transmission and speed change. Time synchronous average (TSA) is one of the most effective methods for vibration monitoring and diagnosis of gearboxes. Traditional TSA technique requires key-phase signal and constant operating speed. So the application of TSA is difficult in many situations such as in the case of gearboxes used in wind power generators and automobiles. A new method to implement TSA without key-phase signal for variable speed condition gearbox is proposed in the paper. The reported method is based on the estimation of instantaneous speed with time-frequency domain filtering and equal angular interval re-sampling of vibration signal. Experimental investigation performed in a variable speed gearbox test rig indicates that the proposed method can eliminate the influence of large speed fluctuation of gearboxes and provide satisfactory TSA results

Fault diagnosis of mechanical drives under non-stationary conditions based on manifold learning of kernel mapping

For the detection of mechanical faults under the operating conditions of varying speeds and loads (such as wind turbines, excavators or helicopters, etc.), a new method for extracting the low-dimensional embedding of vibration data sets of mechanical drives under variable operation conditions is proposed. The hypothesis is that the space spanned by a set of vibration signals can be captured in a varying condition, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifold from a given data set. The embedding manifold generated by vibration signals can be constructed from the feature set of parameters. Taking the variable operation condition into consideration, the kernel mapping is also introduced to improve the identification of submanifolds in terms of the projection distance. With the kernel mapping, the manifold coordinates can accurately capture the differences of the varying operation conditions. Experimental vibration signals obtained from normal and chipped tooth fault of gearbox in varying operation conditions are analyzed in this study. Results show that the proposed method is superior in identifying fault patterns and effective for gearbox condition monitoring

New Insights into the PPAR γ

Diabetic nephropathy (DN) is a severe complication of diabetes and serves as the leading cause of chronic renal failure. In the past decades, angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs) based first-line therapy can slow but cannot stop the progression of DN, which urgently requests the innovation of therapeutic strategies. Thiazolidinediones (TZDs), the synthetic exogenous ligands of nuclear receptor peroxisome proliferator-activated receptor-γ (PPARγ), had been thought to be a promising candidate for strengthening the therapy of DN. However, the severe adverse effects including fluid retention, cardiovascular complications, and bone loss greatly limited their use in clinic. Recently, numerous novel PPARγ agonists involving the endogenous PPARγ ligands and selective PPARγ modulators (SPPARMs) are emerging as the promising candidates of the next generation of antidiabetic drugs instead of TZDs. Due to the higher selectivity of these novel PPARγ agonists on the regulation of the antidiabetes-associated genes than that of the side effect-associated genes, they present fewer adverse effects than TZDs. The present review was undertaken to address the advancements and the therapeutic potential of these newly developed PPARγ agonists in dealing with diabetic kidney disease. At the same time, the new insights into the therapeutic strategies of DN based on the PPARγ agonists were fully addressed

Hybrid-modelling of compact tension energy in high strength pipeline steel using a Gaussian Mixture Model based error compensation

In material science studies, it is often desired to know in advance the fracture toughness of a material, which is related to the released energy during its compact tension (CT) test to prevent catastrophic failure. In this paper, two frameworks are proposed for automatic model elicitation from experimental data to predict the fracture energy released during the CT test of X100 pipeline steel. The two models including an adaptive rule-based fuzzy modelling approach and a double-loop based neural network model, relate the load, crack mouth opening displacement (CMOD) and crack length to the released energies during this test. The relationship between how fracture is propagated and the fracture energy is further investigated in greater detail. To improve the performances of the models, a Gaussian Mixture Model (GMM)-based error compensation strategy which enables one monitor the error distributions of the predicted result is integrated in the model validation stage. This can help isolate the error distribution pattern and to establish the correlations with the predictions from the deterministic models. This is the first time a data-driven approach has been used in this fashion on an application that has conventionally been handled using finite element methods or physical models

Construction and optimization of ecological security pattern based on landscape ecological risk assessment in the affected area of the Lower Yellow River

In the context of urban expansion and climate change, the world is under pressure from multiple ecological risks. Key ecological protection areas play a pivotal role in preserving ecological stability and promoting development. Due to its unique geographical conditions, the Yellow River basin has been facing huge ecological risk pressure. In the affected area of the Lower Yellow River (AALYR) as an agricultural hub, ecological protection has gradually become a key factor restricting the development of cities and agriculture. Taking AALYR as an example, the landscape ecological risk assessment (LERA) system is established based on three aspects “natural environment—human society—landscape pattern”. We construct a comprehensive cumulative resistance surface based on the risk assessment results as the basis for the future study. Ecological corridors are identified by minimum cumulative resistance (MCR) models to establish and optimize Ecological security pattern (ESP) in the AALYR. We found that the landscape ecological risks (LER) in the study area show a uniform spatial distribution, with a slightly higher distribution in the northeast than the southwest. The ecological risk levels are generally high in AALYR, indicating a more severe risk problem in this area. A total of 56 ecological sources were identified, with a total area of 21176 km2. The ecological sensitivity of AALYR was high, and 99 ecological corridors and 59 ecological nodes were extracted. Ecological corridors and nodes were consistently and densely distributed throughout the study area. The network analysis method improves the stability of the network structure after optimization. Based on the key components of the ESP, with the combination of geographical characteristics and local policy planning guidance, we constructed the “One Belt and One Axis, Two Cores and Two Corridors, Four zones” ESP. The study results may offer guidance and suggestions for the construction of ESP and ecological environment protection system in the world’s major river basins, and may also provide information for ecological planning of other similar river basins in the world

Concept for a Future Super Proton-Proton Collider

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage.Comment: 34 pages, 8 figures, 5 table