A novel fault diagnosis approach of gearbox using an embedded sensor fixed gear body

The vibration signals measured from the surface of a gearbox are complex, nonlinear and non-stationary. This paper presents a novel test approach for tooth root crack fault diagnosis of a spur gearbox using an embedded piezoelectric accelerometer. This enhances the ability to extract useful fault information and provide early fault detection in gear transmission systems. The proposed method uses two piezoelectric accelerometers embedded symmetrically on the gear body, which effectively shortens the transmission path of the vibration signals stimulated by a gear fault. The proposed approach is tested by analyzing experimental data from a healthy gear system and systems with cracked gear faults. In order to extract the weak fault information from the experimental data, minimum entropy deconvolution (MED) is first used to eliminate noise from the vibration signals, and then the cyclic autocorrelation function is used to extract the frequency components. The results suggest that the proposed approach can effectively detect 2 mm and 4 mm crack faults, while traditional methods can only detect 4 mm crack faults

Hesperidin Protects against Acute Alcoholic Injury through Improving Lipid Metabolism and Cell Damage in Zebrafish Larvae

Alcoholic liver disease (ALD) is a series of abnormalities of liver function, including alcoholic steatosis, steatohepatitis, and cirrhosis. Hesperidin, the major constituent of flavanone in grapefruit, is proved to play a role in antioxidation, anti-inflammation, and reducing multiple organs damage in various animal experiments. However, the underlying mechanism of resistance to alcoholic liver injury is still unclear. Thus, we aimed to investigate the protective effects of hesperidin against ALD and its molecular mechanism in this study. We established an ALD zebrafish larvae model induced by 350 mM ethanol for 32 hours, using wild-type and transgenic line with liver-specific eGFP expression Tg (lfabp10α:eGFP) zebrafish larvae (4 dpf). The results revealed that hesperidin dramatically reduced the hepatic morphological damage and the expressions of alcohol and lipid metabolism related genes, including cyp2y3, cyp3a65, hmgcra, hmgcrb, fasn, and fads2 compared with ALD model. Moreover, the findings demonstrated that hesperidin alleviated hepatic damage as well, which is reflected by the expressions of endoplasmic reticulum stress and DNA damage related genes (chop, gadd45αa, and edem1). In conclusion, this study revealed that hesperidin can inhibit alcoholic damage to liver of zebrafish larvae by reducing endoplasmic reticulum stress and DNA damage, regulating alcohol and lipid metabolism

Application of Sample Entropy Based LMD-TFPF De-Noising Algorithm for the Gear Transmission System

This paper investigates an improved noise reduction method and its application on gearbox vibration signal de-noising. A hybrid de-noising algorithm based on local mean decomposition (LMD), sample entropy (SE), and time-frequency peak filtering (TFPF) is proposed. TFPF is a classical filter method in the time-frequency domain. However, there is a contradiction in TFPF, i.e., a good preservation for signal amplitude, but poor random noise reduction results might be obtained by selecting a short window length, whereas a serious attenuation for signal amplitude, but effective random noise reduction might be obtained by selecting a long window length. In order to make a good tradeoff between valid signal amplitude preservation and random noise reduction, LMD and SE are adopted to improve TFPF. Firstly, the original signal is decomposed into PFs by LMD, and the SE value of each product function (PF) is calculated in order to classify the numerous PFs into the useful component, mixed component, and the noise component; then short-window TFPF is employed for the useful component, long-window TFPF is employed for the mixed component, and the noise component is removed; finally, the final signal is obtained after reconstruction. The gearbox vibration signals are employed to verify the proposed algorithm, and the comparison results show that the proposed SE-LMD-TFPF has the best de-noising results compared to traditional wavelet and TFPF method

A Hybrid De-Noising Algorithm for the Gear Transmission System Based on CEEMDAN-PE-TFPF

In order to remove noise and preserve the important features of a signal, a hybrid de-noising algorithm based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Permutation Entropy (PE), and Time-Frequency Peak Filtering (TFPF) is proposed. In view of the limitations of the conventional TFPF method regarding the fixed window length problem, CEEMDAN and PE are applied to compensate for this, so that the signal is balanced with respect to both noise suppression and signal fidelity. First, the Intrinsic Mode Functions (IMFs) of the original spectra are obtained using the CEEMDAN algorithm, and the PE value of each IMF is calculated to classify whether the IMF requires filtering, then, for different IMFs, we select different window lengths to filter them using TFPF; finally, the signal is reconstructed as the sum of the filtered and residual IMFs. The filtering results of a simulated and an actual gearbox vibration signal verify that the de-noising results of CEEMDAN-PE-TFPF outperforms other signal de-noising methods, and the proposed method can reveal fault characteristic information effectively

Metformin‐induced autophagy and irisin improves INS‐1 cell function and survival in high‐glucose environment via AMPK/SIRT1/PGC‐1α signal pathway

Abstract In order to explore the protective function of metformin on pancreatic β cells to alleviate insulin resistance and underlying mechanisms, INS‐1 cells were cultured into normal control (N), high glucose (H), high glucose and metformin (H + Met), high glucose and chloroquine (H + CQ), and high glucose and Ex527 (H + Ex527) groups, respectively. Upon 24‐hr cultivation, the proliferation and glucose‐stimulated insulin secretion (GSIS) of INS‐1 cells were determined, and the expression of irisin and other proteins associated with AMPK/SIRT1/PGC‐1α signal pathway, autophagy, and apoptosis was evaluated. Compared with the N group, the cells from the H group revealed lower proliferation, GSIS, and expression of irisin and proteins associated with AMPK/SIRT1/PGC‐1α signal pathway and autophagy, but higher expression of proteins associated with apoptosis; in contrast, metformin could significantly rescue lower cell proliferation, GSIS, and expression of proteins associated with AMPK/SIRT1/PGC‐1α signal pathway and autophagy, as well as irisin, and suppress apoptosis in high‐glucose environment. Meanwhile, autophagy inhibitor CQ and SIRT1 inhibitor Ex527 can block above functions of metformin. Therefore, metformin can promote INS‐1 cell proliferation, enhance GSIS, and suppress apoptosis by activating AMPK/SIRT1/PGC‐1α signal pathway, up‐regulating irisin expression, and inducing autophagy in INS‐1 cells in high‐glucose environment

Joint Machine Selection and Buffer Allocation in Large Split and Merge Manufacturing Systems

This study focuses on the simultaneous optimization of machines and buffers in split and merge production systems. The objective was to minimize the total investment cost under a minimum throughput rate and maximum cycle time constraints. It is challenging to solve this type of stochastic resource allocation problem due to the phenomenon of the combinatorial explosion search space and the inability to obtain closed-form expressions for the optimization model. In this paper, a decomposition-coordination method (DCM) is proposed to optimize the machine types used, the number of machines, and the capacities of buffers of general feed-forward topology systems efficiently and accurately. Instead of directly targeting large-scale systems, the DCM decomposes the original system into several small decoupled systems with added coordination variables and then separately optimizes each decomposed system. An optimal or near-optimal solution is obtained after several iterations of the decoupled system optimization and coordination variable updating. Moreover, we develop a simulated annealing algorithm and non-dominated sorting genetic algorithm-II as benchmark algorithms and provide a parameter calibration analysis of the two metaheuristics. Finally, comprehensive numerical experiments are performed to demonstrate the performances of the DCM, and a multifactorial experimental analysis is conducted to determine the influence of the split and merge system parameters on the performances of the DCM. The results confirmed that the scale of the system, complexity of topology, cycle time constraint, traffic intensity, price ratio, and their interactions significantly influenced the total cost obtained from the DCM, whereas the scale of the system, traffic intensity, and price ratio significantly affected the computation time

An efficient meta-model-based method for uncertainty propagation problems involving non-parameterized probability-boxes

To capture inevitable aleatory and epistemic uncertainties in engineering problems, the probability box (P-box) model is usually an effective quantification tool. The non-parameterized P-box is more general and more flexible than parameterized P-box. While the efficiency of uncertainty propagation methods for non-parameterized P-box is crucial and demands urgently to improve. This paper proposes an efficient meta-model-based method for uncertainty propagation problems involving non-parameterized probability-boxes. In which, the typical Kriging meta-model is first utilized to build the mapping relationship between the non-parameterized P-box variables with the system response. Then, the constructed Kriging model is applied for interval analysis, and the cumulative distribution function of the response function can be obtained using interval Monte Carlo. During building the meta-model, an active learning strategy is proposed and applied to reduce the amount of training data needed from the perspective of exploration and exploitation. Since the prediction variance of Kriging model is not used, the proposed active learning method is not limited to Kriging model and can be applied in any existing meta-models. The numerical examples demonstrate that the proposed method has high accuracy and efficiency in handling nonlinearity, high-dimensional and complex engineering problems

Future-proofing students in higher education with unmanned aerial vehicles technology: A knowledge management case study

In this paper we report experiences in implementing a new course ‘Understanding Drone & Robotics Technology – History, Usage, Ethics & Legal Issues’ at the Singapore Management University framed as a strategic knowledge management (KM) initiative in an institution of higher learning aimed at capturing, sharing and creating new knowledge about disruptive technologies such as unmanned aerial vehicles. We posit the new course as a knowledge innovation initiative (similar to a KM-enabled business case in a corporate setting) in support of the university’s mission and vision so as to deliver new value to students and to stay ahead of the latest technological developments. In line with a ‘normal’ KM initiative, we examine how the new learning and teaching initiative was conceived, pushed forward and eventually launched, creating a new multi-disciplinary learning experience for students, instructors and other stakeholders. We explain the knowledge strategy of the course and use I. Nonaka’s SECI framework to shed light on selected aspects of the pedagogical approach towards achieving the desired learning outcomes. Overall, the paper intends to make a case for more collaborative knowledge leadership as a strategic enabler of multi-disciplinary knowledge innovation in a rapidly changing higher education landscape