Time-frequency Representation -enhanced Transfer Learning for Tool Condition Monitoring during milling of Inconel 718

• Autorzy: Zhou Yuqing , Sun Wei , Ye Canyang , Peng Bihui , Fang Xu , Lin Canyu , Wang Gonghai , Kumar Anil , Sun Weifang

Identyfikatory

DOI

10.17531/ein/165926

• Języki publikacji: EN

Abstrakty

EN

Accurate tool condition monitoring (TCM) is important for the development and upgrading of the manufacturing industry. Recently, machine-learning (ML) models have been widely used in the field of TCM with many favorable results. Nevertheless, in the actual industrial scenario, only a few samples are available for model training due to the cost of experiments, which significantly affects the performance of ML models. A time-series dimension expansion and transfer learning (TL) method is developed to boost the performance of TCM for small samples. First, a time-frequency Markov transition field (TFMTF) is proposed to encode the cutting force signal in the cutting process to two-dimensional images. Then, a modified TL network is established to learn and classify tool conditions under small samples. The performance of the proposed TFMTF-TL method is demonstrated by the benchmark PHM 2010 TCM dataset. The results show the proposed method effectively obtains superior classification accuracies for small samples and outperforms other four benchmark methods.

Słowa kluczowe

EN

tool condition monitoring; time-frequency analysis; Markov Transition Field; transfer learning

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 2
• Strony: art. no. 165926
• Opis fizyczny: Bibliogr. 33 poz., rys., tab., wykr.

Twórcy

autor

Zhou Yuqing

• School of information science and technology, Northwest University, China
• College of Mechanical and Electrical Engineering, Jiaxing Nanhu University, China

• https://orcid.org/0000-0002-8580-5427

autor

Sun Wei

• College of mechanical and electrical engineering, Wenzhou University, China

autor

Ye Canyang

• Zhejiang Meishuo Electric Technology Co., Ltd, China

autor

Peng Bihui

• Zhejiang Meishuo Electric Technology Co., Ltd, China

autor

Fang Xu

• Zhejiang Meishuo Electric Technology Co., Ltd, China

autor

Lin Canyu

• Zhejiang Meishuo Electric Technology Co., Ltd, China

autor

Wang Gonghai

• College of Mechanical and Electrical Engineering, Jiaxing Nanhu University, China

• https://orcid.org/0000-0003-1581-6267

autor

Kumar Anil

• College of mechanical and electrical engineering, Wenzhou University, China

• https://orcid.org/0000-0001-6675-1657

autor

Sun Weifang

• College of mechanical and electrical engineering, Wenzhou University, China

• https://orcid.org/0000-0002-4181-1606

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