Acoustic emission sources localization of laser cladding metallic panels using improved fruit fly optimization algorithm-based independent variational mode decomposition

2022 ◽  
Vol 166 ◽  
pp. 108514
Author(s):  
Yang Li ◽  
Feiyun Xu
Keyword(s):  
Acoustic Emission ◽  
Laser Cladding ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Emission Sources ◽  
Fruit Fly Optimization Algorithm ◽  
Variational Mode Decomposition ◽  
Fruit Fly Optimization ◽  
Mode Decomposition ◽  
Metallic Panels

scholarly journals Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 400 ◽  
Author(s):  
Zhou ◽  
Guo ◽  
Wang ◽  
Du ◽  
Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Fruit Fly ◽  
Permutation Entropy ◽  
Measured Signal ◽  
Fruit Fly Optimization Algorithm ◽  
Variational Mode Decomposition ◽  
Fruit Fly Optimization ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Combined Parameters

In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decomposition layer number k and the penalty factor α, if the parameters are not properly selected, there will be over-decomposition or under-decomposition. In order to find an approach to determine the parameters adaptively, a method to optimize VMD by using the immune fruit fly optimization algorithm (IFOA) is proposed in this paper. In this method, permutation entropy is used as the fitness function, firstly, the immune fruit fly optimization algorithm is used to search the combined parameters of k and α in VMD, searching for the best combination parameters of k and α by iteration, and then uses the combined parameters to perform VMD, finally, the center frequency is determined through frequency spectrum analysis. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a wind turbine gearbox, and the fault frequency is successfully extracted. Using ensemble empirical mode decomposition (EEMD) and singular spectrum decomposition (SSD) to compare with the proposed method, which validated feasibility of the proposed method.

scholarly journals A Hybrid Model Based on Ensemble Empirical Mode Decomposition and Fruit Fly Optimization Algorithm for Wind Speed Forecasting

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Zongxi Qu ◽  
Kequan Zhang ◽  
Jianzhou Wang ◽  
Wenyu Zhang ◽  
Wennan Leng
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Ensemble Empirical Mode Decomposition ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Wind Speed Forecasting ◽  
Forecasting Models ◽  
Mode Decomposition

As a type of clean and renewable energy, the superiority of wind power has increasingly captured the world’s attention. Reliable and precise wind speed prediction is vital for wind power generation systems. Thus, a more effective and precise prediction model is essentially needed in the field of wind speed forecasting. Most previous forecasting models could adapt to various wind speed series data; however, these models ignored the importance of the data preprocessing and model parameter optimization. In view of its importance, a novel hybrid ensemble learning paradigm is proposed. In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. To estimate the forecasting ability of the proposed model, 15 min wind speed data for wind farms in the coastal areas of China was performed to forecast as a case study. The empirical results show that the proposed hybrid model is superior to some existing traditional forecasting models regarding forecast performance.

Adaptive chaos fruit fly optimization algorithm

2013 ◽  
Vol 33 (5) ◽  
pp. 1313-1316 ◽  
Author(s):  
Junying HAN ◽  
Chengzhong LIU
Keyword(s):  
Optimization Algorithm ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization

Research on Enhancement Technology of Conveyor Belt Fault Images Based on Fruit Fly Optimization Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 685-689
Author(s):  
Chunqing Ye ◽  
Changyun Miao ◽  
Xianguo Li ◽  
Yanli Yang
Keyword(s):  
Optimization Algorithm ◽  
Detection System ◽  
Fruit Fly ◽  
Conveyor Belt ◽  
Recognition Algorithm ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Wire Ropes ◽  
Fault Recognition ◽  
Steel Cord

In this research, we studied the fault recognition algorithm of steel cord conveyor belt, and obtained the wire ropes image by adopting the detection system of steel cord conveyor belt, so that the fault recognition algorithm of steel cord conveyor belt was proposed based on Fruit fly optimization algorithm. As we know that the fruit fly optimization algorithm is used for fault detection of the processing steel cord conveyor belt image and for obtaining the fault image. In the MATLAB environment, the algorithm process was designed and verified in terms of the effectiveness and accuracy. The experimental results show that with fast speed and high accuracy in detecting the fault image of steel cord conveyor belt rapidly and accurately, and in classifying scratch from fracture the proposed algorithm is suitable for the fault recognition of steel cord conveyor belt automatically.

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