fnctId=thesis,fnctNo=98
Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction
- 링크
- https://doi.org/10.3390/pr8040456
- 작성자
- 김백천
- 저자
- 김종근, 이한수, 전정우, 김종문, 이현욱, 김성신
- 발행사항
- 발행일
- 2020.04.21
- 국문초록
- 영문초록
- Machining processes are critical and widely used components in the manufacturing industry because they help to precisely make products and reduce production time. To keep the previous advantages, a machine tool should be installed at the designated place and condition of the machine tool should be maintained appropriately to working environment. In various maintenance methods for keeping the condition of machine tool, condition-based maintenance can be robust to unpredicted accidents and reduce maintenance costs. Tool monitoring and diagnosis are some of the most important components of the condition based maintenance. This paper proposes stacked auto-encoder based CNC machine tool diagnosis using discrete wavelet transform feature extraction to diagnose a machine tool. The diagnosis model, which only uses cutting force data, cannot sufficiently reflects tool condition. Hence, we modeled diagnosis model using features extracted from a cutting force, a current signal, and coefficients of the discrete wavelet transform. The experimental results showed that the model which uses feature data has better performance than the model that uses only cutting force data. The feature based models are lower false negative rate (FNR) and false positive rate. Moreover, squared prediction error using normalized residual vector also reduced FNR because normalization reduces weight bias.
- 일반텍스트
- 첨부파일