fnctId=thesis,fnctNo=98
Fault Detection for CNC Machine Tools Using Auto-Associative Kernel Regression Based on Empirical Mode Decomposition
- 링크
- https://doi.org/10.3390/pr10122529
- 작성자
- 김백천
- 저자
- 정승환, 김민석, 김백천, 김진용, 김은경, 김종근, 이현욱, 김성신
- 발행사항
- 발행일
- 2022.11.28
- 국문초록
- 영문초록
- In manufacturing processes using computerized numerical control (CNC) machines, machine tools are operated repeatedly for a long period for machining hard and difficult-to-machine materials, such as stainless steel. These operating conditions frequently result in tool breakage. The failure of machine tools significantly degrades the product quality and efficiency of the target process. To solve these problems, various studies have been conducted for detecting faults in machine tools. However, the most related studies used only the univariate signal obtained from CNC machines. The fault-detection methods using univariate signals have a limitation in that multivariate models cannot be applied. This can restrict in performance improvement of the fault detection. To address this problem, we employed empirical mode decomposition to construct a multivariate dataset from the univariate signal. Subsequently, auto-associative kernel regression was used to detect faults in the machine tool. To verify the proposed method, we obtained a univariate current signal measured from the machining center in an actual industrial plant. The experimental results demonstrate that the proposed method successfully detects faults in the actual machine tools.
- 일반텍스트
- 첨부파일