Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process

2020 ◽  
Author(s):  
Yuqing Zhou ◽  
Bintao Sun ◽  
Weifang Sun ◽  
Zhi Lei
Keyword(s):  
Tool Wear ◽  
Extreme Learning Machine ◽  
Condition Monitoring ◽  
Milling Process ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Wear Condition

Tool wear condition monitoring based on wavelet transform and improved extreme learning machine

2019 ◽  
Vol 234 (5) ◽  
pp. 1057-1068 ◽  
Author(s):  
Soufiane Laddada ◽  
Med. Ouali Si-Chaib ◽  
Tarak Benkedjouh ◽  
Redouane Drai
Keyword(s):  
Wavelet Transform ◽  
Tool Wear ◽  
Extreme Learning Machine ◽  
Cutting Tools ◽  
Remaining Useful Life ◽  
Machining Process ◽  
Non Linear ◽  
Useful Life ◽  
Learning Machine ◽  
Wear Condition

In machining process, tool wear is an inevitable consequence which progresses rapidly leading to a catastrophic failure of the system and accidents. Moreover, machinery failure has become more costly and has undesirable consequences on the availability and the productivity. Consequently, developing a robust approach for monitoring tool wear condition is needed to get accurate product dimensions with high quality surface and reduced stopping time of machines. Prognostics and health management has become one of the most challenging aspects for monitoring the wear condition of cutting tools. This study focuses on the evaluation of the current health condition of cutting tools and the prediction of its remaining useful life. Indeed, the proposed method consists of the integration of complex continuous wavelet transform (CCWT) and improved extreme learning machine (IELM). In the proposed IELM, the hidden layer output matrix is given by inverting the Moore–Penrose generalized inverse. After the decomposition of the acoustic emission signals using CCWT, the nodes energy of coefficients have been taken as relevant features which are then used as inputs in IELM. The principal idea is that a non-linear regression in a feature space of high dimension is involved by the extreme learning machine to map the input data via a non-linear function for generating the degradation model. Then, the health indicator is obtained through the exploitation of the derived model which is in turn used to estimate the remaining useful life. The method was carried out on data of the real world collected during various cuts of a computer numerical controlled tool.

scholarly journals Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 95491-95502 ◽  
Author(s):  
Yuqing Zhou ◽  
Weifang Sun
Keyword(s):  
Tool Wear ◽  
Condition Monitoring ◽  
Milling Process ◽  
Current Sensors ◽  
Wear Condition

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

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