scholarly journals Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7702
Author(s):  
Shu Han ◽  
Xiaoming Liu ◽  
Yan Yang ◽  
Hailin Cao ◽  
Yuanhong Zhong ◽  
...  
Keyword(s):  
Production Equipment ◽  
Signal Decomposition ◽  
Reconstruction Method ◽  
Intelligent Algorithm ◽  
Excellent Performance ◽  
Marine Predator ◽  
Mode Decomposition ◽  
Transmission Equipment ◽  
Parameters Selection ◽  
Selection Of

With the development of modern industry and scientific technology, production equipment plays an increasingly important role in military and industrial production, and the fault detection signal of gears and bearings state in transmission equipment becomes very important. Therefore, this paper proposes a gear-bearing composite fault signal decomposition and reconstruction method, which combines the marine predator algorithm (MPA) and variational mode decomposition (VMD) technologies. For the parameters’ selection of VMD, the optimization algorithm allows us to quickly and accurately obtain the results with the best kurtosis correlation index after signal decomposition and reconstruction. The experiments demonstrate the excellent performance of our method in the field of separation and denoising mixed gear-bearing fault signals.

A Systematic Research on Reconstructing the Accident Velocity with Braking Traces

2012 ◽  
Vol 591-593 ◽  
pp. 2114-2117
Author(s):  
Chao Wei ◽  
Miao Xin Nie
Keyword(s):  
Environmental Factors ◽  
Systematic Research ◽  
Reconstruction Method ◽  
Vehicle Speed ◽  
Factors Influencing ◽  
Parameters Selection ◽  
One To One ◽  
Important Basis ◽  
Reconstructed Model ◽  
Selection Of

Braking trace is the important basis to reconstruct the speed of the accident vehicle. There does not exist one-to-one relationship between the barking trace and the vehicle speed. It needs to have a further research on the factors influencing their corresponding relations, otherwise it may appear great errors to reproduce the vehicle speed with the braking trace. This paper analyzes the influence of people, cars, roads, environmental factors on the corresponding relationship between the initial braking velocity and braking traces, explores the use conditions and parameters selection of the reconstructed model in the standard GA/T643-2006, and the experiment is designed to research on the uncertainty of the reconstruction of the model. Besides, it has proposed the experimental reconstruction method.

scholarly journals Circular Economy Strategies for Equipment Lifetime Extension: A Systematic Review

2021 ◽  
Vol 13 (3) ◽  
pp. 1117
Author(s):  
Alessandro Fontana ◽  
Andrea Barni ◽  
Deborah Leone ◽  
Maurizio Spirito ◽  
Agata Tringale ◽  
...  
Keyword(s):  
Life Cycle ◽  
Circular Economy ◽  
Resource Scarcity ◽  
Production Equipment ◽  
Environmental Standards ◽  
Multiple Strategies ◽  
Medium Enterprise ◽  
Equipment Lifetime ◽  
Selection Of ◽  
Lifetime Extension

Even if the economy nowadays is still locked into a linear model of production, tighter environmental standards, resource scarcity and changing consumer expectations are forcing organizations to find alternatives to lighten their impacts. The concept of Circular Economy (CE) is to an increasing extent treated as a solution to this series of challenges. That said, the multitude of approaches and definitions around CE and Life Cycle Extension Strategies (LCES) makes it difficult to provide (Small and Medium Enterprise) SMEs with a consistent understanding of the topic. This paper aims at bridging this gap by providing a systematic literature review of the most prominent papers related to the CE and lifetime extension, with a particular focus on the equipment and machinery sector. A taxonomy was used to define and cluster a subset of selected papers to build a homogeneous approach for understanding the multiple strategies used in the industry, and the standards in maintenance and remanufacturing strategies. As a final research step, we also propose a Strategy Characterization Framework (SCF) to build the ground for the selection of the best strategy to be applied for production equipment life cycle extension on several industrial use cases.

scholarly journals Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3125
Author(s):  
Zou ◽  
Chen ◽  
Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Field Experiments ◽  
Low Frequency ◽  
Vibration Signal ◽  
Micro Electro Mechanical System ◽  
Ensemble Empirical Mode Decomposition ◽  
Reconstruction Method ◽  
Monitoring Methods ◽  
Dynamic Displacement ◽  
Mode Decomposition

Considering the lack of precision in transforming measured micro-electro-mechanical system (MEMS) accelerometer output signals into elevation signals, this paper proposes a bridge dynamic displacement reconstruction method based on the combination of ensemble empirical mode decomposition (EEMD) and time domain integration, according to the vibration signal traits of a bridge. Through simulating bridge analog signals and verifying a vibration test bench, four bridge dynamic displacement monitoring methods were analyzed and compared. The proposed method can effectively eliminate the influence of low-frequency integral drift and high-frequency ambient noise on the integration process. Furthermore, this algorithm has better adaptability and robustness. The effectiveness of the method was verified by field experiments on highway elevated bridges.

scholarly journals Best Selection of Generative Adversarial Networks Hyper-Parameters using Genetic Algorithm

2020 ◽  
Author(s):  
Fajr Alarsan ◽  
Mamoon Younes
Keyword(s):  
Genetic Algorithm ◽  
Programming Language ◽  
Loss Function ◽  
Generative Adversarial Networks ◽  
Accuracy Function ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Parameters Selection ◽  
Selection Of

Abstract Generative Adversarial Networks (GANs) are most popular generative frameworks that have achieved compelling performance. They follow an adversarial approach where two deep models generator and discriminator compete with each other In this paper, we propose a Generative Adversarial Network with best hyper-parameters selection to generate fake images for digits number 1 to 9 with generator and train discriminator to decide whereas the generated images are fake or true. Using Genetic Algorithm technique to adapt GAN hyper-parameters, the final method is named GANGA:Generative Adversarial Network with Genetic Algorithm. Anaconda environment with tensorflow library facilitates was used, python as programming language also used with needed libraries. The implementation was done using MNIST dataset to validate our work. The proposed method is to let Genetic algorithm to choose best values of hyper-parameters depending on minimizing a cost function such as a loss function or maximizing accuracy function. GA was used to select values of Learning rate, Batch normalization, Number of neurons and a parameter of Dropout layer.

scholarly journals On the Origin of the Standardization Sensitivity in RegEM Climate Field Reconstructions*

2008 ◽  
Vol 21 (24) ◽  
pp. 6710-6723 ◽  
Author(s):  
Jason E. Smerdon ◽  
Alexey Kaplan ◽  
Diana Chang
Keyword(s):  
Standard Deviation ◽  
Missing Values ◽  
Mean Field ◽  
Alternative Methods ◽  
Reconstruction Method ◽  
Climate Data ◽  
The Mean ◽  
Calibration Interval ◽  
Reconstruction Interval ◽  
Selection Of

Abstract The regularized expectation maximization (RegEM) method has been used in recent studies to derive climate field reconstructions of Northern Hemisphere temperatures during the last millennium. Original pseudoproxy experiments that tested RegEM [with ridge regression regularization (RegEM-Ridge)] standardized the input data in a way that improved the performance of the reconstruction method, but included data from the reconstruction interval for estimates of the mean and standard deviation of the climate field—information that is not available in real-world reconstruction problems. When standardizations are confined to the calibration interval only, pseudoproxy reconstructions performed with RegEM-Ridge suffer from warm biases and variance losses. Only cursory explanations of this so-called standardization sensitivity of RegEM-Ridge have been published, but they have suggested that the selection of the regularization (ridge) parameter by means of minimizing the generalized cross validation (GCV) function is the source of the effect. The origin of the standardization sensitivity is more thoroughly investigated herein and is shown not to be associated with the selection of the ridge parameter; sets of derived reconstructions reveal that GCV-selected ridge parameters are minimally different for reconstructions standardized either over both the reconstruction and calibration interval or over the calibration interval only. While GCV may select ridge parameters that are different from those that precisely minimize the error in pseudoproxy reconstructions, RegEM reconstructions performed with truly optimized ridge parameters are not significantly different from those that use GCV-selected ridge parameters. The true source of the standardization sensitivity is attributable to the inclusion or exclusion of additional information provided by the reconstruction interval, namely, the mean and standard deviation fields computed for the complete modeled dataset. These fields are significantly different from those for the calibration period alone because of the violation of a standard EM assumption that missing values are missing at random in typical paleoreconstruction problems; climate data are predominantly missing in the preinstrumental period when the mean climate was significantly colder than the mean of the instrumental period. The origin of the standardization sensitivity therefore is not associated specifically with RegEM-Ridge, and more recent attempts to regularize the EM algorithm using truncated total least squares could theoretically also be susceptible to the problems affecting RegEM-Ridge. Nevertheless, the principal failure of RegEM-Ridge arises because of a poor initial estimate of the mean field, and therefore leaves open the possibility that alternative methods may perform better.

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