Health assessment and life prediction of cutting tools based on support vector regression

By Tarak Benkedjouh, Kamal Medjaher, Noureddine Zerhouni, Said Rechak. Journal of Intelligent Manufacturing, 2013

376 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/d0a716edfab0f15d7736b267b35b7b6a923b5051

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Naipeng Li
2. Yaguo Lei
3. Ningbo Li
4. Enrico Zio
5. Liang Guo
6. Tao Yan
7. Boyuan Yang
8. Ruonan Liu

In Electrical and Electronic Engineering

1. Noureddine Zerhouni
2. Enrico Zio
3. Robert X. Gao
4. Soundar Kumara *
5. Connor Jennings
6. Janis P. Terpenny
7. Kamal Medjaher
8. Boyuan Yang

In Mechanical Engineering

1. Naipeng Li
2. Yaguo Lei
3. Noureddine Zerhouni
4. Enrico Zio
5. Boyuan Yang
6. Ruonan Liu
7. Ningbo Li
8. Kamal Medjaher

Citations in the corpus

• Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics (2015, methodology): “Following that, rather than a classical approach [26], [27], features are extracted/selected (from a decomposed signal) in a new manner: First, trigonometric functions are applied to extract features and are then smoothed to remove unwanted noisy parts.”
• Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction (2014, positive): “One way to do data-driven RUL prediction is by first estimating the current health status of the desired component and when the degradation exceeds the alarm threshold, the algorithms start predicting the RUL (Zhang 2003; Gorjian et al. 2009; Benkedjouh et al. 2013).”
• Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture (2019, background): “The IF point is often determined experimentally due to the difference between machines at failure [29], [30].”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “Support vector regression (SVR) is the common application form of SVM in the field of prognostics [128].”
• Tool condition monitoring techniques in milling process — a review (2020, background): “TCMs, including Probabilistic Neural Network (PNN) [14], Support Vector Regression (SVR) [56], Support Vector Machine”
• A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests (2017, methodology): “[41] presented a method for tool wear assessment and remaining useful life prediction using SVMs.”
• Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015, neutral): “The data-driven approaches are generally divided into two categories: (1) the artificial intelligence approaches that include neural network (NN) [21–23] and fuzzy logic [24,25]; and (2) the statistical approaches that include the Gaussian process (GP) regression [26,27], relevance/support vector ma”