Fault Diagnosis of Tool Wear Based on Weak Feature Extraction and GA-B-spline Network

In view of the strong background noise involved in vibration signal of tool wear and the difficulty to obtain fault frequencies, it is important to de-noise before the further processing. Independent component analysis (ICA) was recently developed to deal with the blind source separation problem and it is particularly effective in the separation of non-Gaussian signals. This paper proposed a signal-noise-separated method with ICA. Then, de-noise signals are decomposed with empirical mode decomposition (EMD). Finally, Tool wear by identified by GA-B-spline neural network. B-spline networks is traditionally trained by using gradient-based methods, this may fall into local minimum during the learning process. In this paper, it is trained using genetic algorithms to search for global optimization. The experimental results show that the diagnosis approach put forward in this paper can effectively identify tool wear fault patterns in noise background and it has great application potential in health condition monitoring of tool wear.