scholarly journals Optimized ELM based on Whale Optimization Algorithm for gearbox diagnosis

2019 ◽  
Vol 255 ◽  
pp. 02003
Author(s):  
M. Firdaus Isham ◽  
M. Salman Leong ◽  
M. H. Lim ◽  
Z. A.B. Ahmad
Keyword(s):  
Optimization Algorithm ◽  
Learning Algorithm ◽  
Whale Optimization Algorithm ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Gearbox Vibration ◽  
Learning Machine ◽  
Hidden Layer ◽  
New Diagnosis

Extreme learning machine (ELM) is a fast and quick learning algorithm with better generalization performance. However, the randomness of input weight and hidden layer bias may affect the overall performance of ELM. This paper proposed a new approach to determine the optimized values of input weight and hidden layer bias for ELM using whale optimization algorithm (WOA), which we call WOA-ELM. An online gearbox vibration signals is used in this study. Empirical mode decomposition (EMD) and complementary mode decomposition (CEEMD) are used to decompose the signals into sub-signals known as intrinsic mode functions (IMFs). Then, statistical features are extracted from selected IMFs. WOA-ELM is used for classification of healthy and faulty condition of gearbox. The result shows that WOA-ELM provide better classification result as compared with conventional ELM. Therefore, this study provide a new diagnosis approach for gearbox application.

scholarly journals MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4622 ◽  
Author(s):  
Huichao Yan ◽  
Ting Xu ◽  
Peng Wang ◽  
Linmei Zhang ◽  
Hongping Hu ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Whale Optimization Algorithm ◽  
Signal Denoising ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Baseline Drift ◽  
Mode Decomposition ◽  
Whale Optimization

Underwater acoustic technology is an important means of detecting the ocean. Due to the complex influence of the marine environment, there is a lot of noise and baseline drift in the signals collected by hydrophones. In order to solve this problem, this paper proposes a denoising and baseline drift removal algorithm for MEMS vector hydrophone based on whale-optimized variational mode decomposition (VMD) and correlation coefficient (CC). Firstly, the power spectrum entropy (PSE), which reflects the variation characteristics of the signal frequency is selected as the fitness function of the whale-optimization algorithm to find the parameters (K,α) of the VMD. It is easier to find the global optimal solution of the parameters by combining the whale-optimization algorithm. Then, using the VMD algorithm after obtaining the parameters, the original signal is decomposed to obtain the intrinsic mode functions (IMFs), and calculating the correlation coefficients (CCs) between the IMFs and the original signal. Finally, the CC threshold is used to remove the noise IMFs, and the rest of the useful IMFs are reconstructed to complete the denoising and baseline drift removal process of the original signals. In the simulation experiments, the algorithm proposed in this paper shows better performance by comparing conventional digital signal-processing methods and the related algorithms proposed recently. Applied in the experiments of a MEMS hydrophone, the effectiveness of the proposed algorithm is also verified. This algorithm can provide new ideas for signal denoising and baseline drift removal.

scholarly journals Classification of Aggressive Behaviors Based on sEMG Feature Extraction and Machine Learning Algorithm

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 656-656
Author(s):  
Youngjun Kim ◽  
Uchechukuwu David ◽  
Yeonsik Noh
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Learning Machine

Abstract New surface electromyography (sEMG) feature extraction approach combined with Empirical Mode Decomposition (EMD) and Dispersion Entropy (DisEn) is proposed for classifying aggressive and normal behaviors from sEMG data. In this study, we used the sEMG physical action dataset from the UC Irvine Machine Learning repository. The raw sEMG was decomposed with EMD to obtain a set of Intrinsic Mode Functions (IMF). The IMF, which includes the most discriminant feature for each action, was selected based on the analysis by Hibert Transform (HT) in the time-frequency domain. Next, the DisEn of the selected IMF was calculated as a corresponding feature. Finally, the DisEn value was tested using five different classifiers, such as LDA, Quadratic DA, k-NN, SVM, and Extreme Learning Machine (ELM) for the classification task. Among these ML algorithms, we achieved classification accuracy, sensitivity, and specificity with ELM as 98.44%, 100%, and 96.72%, respectively.

scholarly journals An Integrated Framework of GRU Based on Improved Whale Optimization Algorithm for Flood Prediction

2021 ◽  
Author(s):  
Chunlei Ji ◽  
Tian Peng ◽  
Chu Zhang ◽  
Lei Hua ◽  
Wei Sun
Keyword(s):  
Time Series ◽  
Optimization Algorithm ◽  
Flood Control ◽  
Control Strategies ◽  
Whale Optimization Algorithm ◽  
Flood Prediction ◽  
Hybrid Strategy ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Using Data

Abstract Accurate prediction of floods is the first step in formulating flood control strategies and reducing flood disasters. This research proposes a deep learning model based on Gate Recurrent Unit (GRU), Random Forest Algorithm (RF), Whale Optimization Algorithm (WOA) and Optimal Variational Mode Decomposition (OVMD) for flood prediction. First, the random historical time series is decomposed using OVMD. Secondly, combined with the RF feature importance measurement, select features with high importance to obtain the optimal input set. Third, use the GRU model to predict all sub-models, and use the WOA algorithm to optimize the hyperparameters in the GRU model. This study also proposes a hybrid strategy to improve the traditional WOA algorithm and enhance the optimization ability of the WOA algorithm. Finally, the prediction results of all sub-modes were aggregated to generate the final prediction result. The model was validated using data from three hydrological stations in the upper, middle and lower reaches of the Minjiang river basin in China. Through the results of the experiment, it can be seen that the proposed prediction model can effectively predict the flood time series, and has better accuracy than other models.

Color difference classification of solid color printing and dyeing products based on optimization of the extreme learning machine of the improved whale optimization algorithm

2019 ◽  
Vol 90 (2) ◽  
pp. 135-155 ◽  
Author(s):  
Zhiyu Zhou ◽  
Chao Wang ◽  
Jianxin Zhang ◽  
Zefei Zhu
Keyword(s):  
Extreme Learning Machine ◽  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Color Difference ◽  
Classification Model ◽  
Initial Population ◽  
Whale Optimization ◽  
Color Printing ◽  
Learning Machine ◽  
Hidden Layer

To mitigate the problem of low classification accuracy in solid color printing and dyeing, a color difference classification model based on the differential evolution (DE) improved whale optimization algorithm (WOA) for extreme learning machine (ELM) optimization, named the DE–WOA–ELM, was developed in this study. Considering that the initial population of the WOA has a significant influence on the solution speed and quality, DE was used to generate a more suitable initial population for the WOA by avoiding local optima, thereby improving the performance. The method used an excellent global search ability to improve the WOA for optimization and obtained an optimal parameter combination for the ELM. Thus, the problem of randomly initializing the input weight and the hidden layer bias of the ELM, which leads to a nonuniform training model and unstable algorithm, was solved. Finally, by optimizing the input weight and hidden layer bias, the color difference classification model of the ELM with a strong generalization ability was constructed. The results of the color difference classification experiments on fabric images collected under standard light sources show that the average classification accuracy for the dataset is increased by 2.15%, 11.06%, 12.11%, and 0.47% compared with those of the ELM, support vector machine, back propagation neural network, and kernel ELM, respectively.

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