scholarly journals A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network

Energies ◽  
2012 ◽  
Vol 5 (2) ◽  
pp. 355-370 ◽  
Author(s):  
Bangzhu Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Empirical Mode Decomposition ◽  
Carbon Price ◽  
Price Prediction ◽  
Mode Decomposition ◽  
Artificial Neural

Damage detection of structures using signal processing and artificial neural networks

2019 ◽  
Vol 23 (5) ◽  
pp. 884-897 ◽  
Author(s):  
Seyed Bahram Beheshti Aval ◽  
Vahid Ahmadian ◽  
Mohammad Maldar ◽  
Ehsan Darvishan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Signal Processing ◽  
Damage Detection ◽  
Empirical Mode Decomposition ◽  
Degree Of Freedom ◽  
Benchmark Problem ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Artificial Neural

This article presents a signal-based seismic structural health monitoring technique for damage detection and evaluating damage severity of a multi-story frame subjected to an earthquake event. As a case study, this article is focused on IASC–ASCE benchmark problem to provide the possibility for side-by-side comparison. First, three signal processing techniques including empirical mode decomposition, Hilbert vibration decomposition, and local mean decomposition, categorized as instantaneous time–frequency methods, have been compared to find a method with the best resolution in extracting frequency responses. Time-varying single degree of freedom and multiple degree of freedom models are used since real vibration signals are nonstationary and nonlinear in nature. Based on the results, empirical mode decomposition has proved to outperform than the others. Second, empirical mode decomposition is used to extract the acceleration response of the sensors. Next, a two-stage artificial neural network is used to classify damage patterns. The first artificial neural network identifies location and severity of damage and the second one calculates the severity of damage for the entire structure. IASC–ASCE benchmark problem is used to validate the proposed procedure. By taking advantage of signal processing and artificial intelligence techniques, damage detection of structures was successfully carried out in three levels including damage occurrence, damage severity, and the location of damage.

Dynamic Response Analysis of Gearbox to Improve Fault Detection Using Empirical Mode Decomposition and Artificial Neural Network Techniques

2021 ◽  
Author(s):  
Ramchandra Ganapati Desavale ◽  
Prakash M Jadhav ◽  
Nagaraj V. Dharwadkar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Empirical Mode Decomposition ◽  
Condition Monitoring ◽  
Response Analysis ◽  
Cyber Physical System ◽  
Time Intervals ◽  
Mode Decomposition ◽  
Artificial Neural ◽  
Performance Results

Abstract Since the last decade, gearbox systems have been requiring increasing power, and consequently, the complexity of systems has escalated. Inevitably, this complexity has resulted in the need for the troubleshooting of gearbox systems. With a growing trend of health monitoring in rotating machines, diagnostic and prognostic studies have become focused on diagnosing existing and potential failures in gearbox systems. In this context, this study develops the architecture of the cloud-based cyber-physical system for condition monitoring of gearbox. Empirically collected vibration signals of gear wear at various time intervals are processed using Empirical Mode Decomposition (EMD) algorithm. A Euclidian-based distance evaluation technique is applied to select the most sensitive features of car gear wear. Artificial Neural Network is trained using extracted features to monitor the gearbox for the future dataset. Comparison of the performance results revealed that the ANN is superior to the other EMD methods. The present methodology was found efficient and reliable for condition monitoring of industrial gearbox.

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