Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

Bin Yang; Yaguo Lei; Xiang Li; Naipeng Li; Asoke K. Nandi

doi:10.1109/JAS.2023.124083

Volume 11 Issue 4

Apr. 2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2024 > 11(4): 932-945

B. Yang, Y. Lei, X. Li, N. Li, and A. Nandi, “Label recovery and trajectory designable network for transfer fault diagnosis of machines with incorrect annotation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 4, pp. 932–945, Apr. 2024. doi: 10.1109/JAS.2023.124083

Citation:

B. Yang, Y. Lei, X. Li, N. Li, and A. Nandi, “Label recovery and trajectory designable network for transfer fault diagnosis of machines with incorrect annotation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 4, pp. 932–945, Apr. 2024. doi: 10.1109/JAS.2023.124083

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Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

doi: 10.1109/JAS.2023.124083

Funds: This work was supported in part by the National Key R&D Program of China (2022YFB3402100), the National Science Fund for Distinguished Young Scholars of China (52025056), the National Natural Science Foundation of China (52305129), the China Postdoctoral Science Foundation (2023M732789), the China Postdoctoral Innovative Talents Support Program (BX20230290), the Open Foundation of Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment (2022JXKFJJ01), and the Fundamental Research Funds for Central Universities

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Author Bio:
Bin Yang (Member, IEEE) received the B.S. and M.S. degrees in mechatronics engineering from Chang’an University in 2014 and 2017, respectively, and the Ph.D. degree in mechanical engineering from Xi’an Jiaotong University in 2022. He is currently an Assistant Professor with the School of Mechanical Engineering, Xi’an Jiaotong University. He was also a visiting Ph.D. student of the Centre of Maintenance Optimization and Reliability Engineering (C-MORE) at the University of Toronto, Canada. His research interests include intelligent fault diagnosis and condition monitoring of machines

Yaguo Lei (Senior Member, IEEE) received the B.S. and Ph.D. degrees in mechanical engineering from Xi’an Jiaotong University in 2002 and 2007, respectively. He is currently a Full Professor of mechanical engineering at Xi’an Jiaotong University. Prior to joining Xi’an Jiaotong University in 2010, he was a Postdoctoral Research Fellow with the University of Alberta, Canada. He was also an Alexander von Humboldt Fellow with the University of Duisburg-Essen, Germany. His research interests include machinery condition monitoring and intelligent maintenance, dynamic modeling, signal processing, intelligent fault diagnosis, and remaining useful life prediction. He has pioneered many fault diagnosis and prognosis methods, and applied them to industrial areas, such as wind turbines, trains and industrial robots, etc.Dr. Lei is a Fellow of ASME, IET and ISEAM, a Senior Member of IEEE, and a Senior Member of Chinese Mechanical Engineering Society, Operations Research Society of China and Chinese Association of Automation, respectively. He is currently an Associate Editor or Editorial Board Member of more than ten journals, including IEEE Transactions on Industrial Electronics and Mechanical Systems and Signal Processing, etc.

Xiang Li (Senior Member, IEEE) received the B.S. degree in engineering mechanics and the Ph.D. degree in mechanics from Tianjin University in 2012 and 2017, respectively. He is currently an Associate Professor with the School of Mechanical Engineering, Xi’an Jiaotong University. Prior to joining Xi’an Jiaotong University, he was a Postdoctoral Fellow in the Center for Intelligent Maintenance Systems (IMS) at University of Cincinnati, USA, and an Associate Professor at Northeastern University. He was also a Visiting Scholar in School of Engineering at University of California, Merced, USA. His research interests include industrial artificial intelligence, industrial big data, machinery fault diagnosis and prognostics, etc.

Naipeng Li (Member, IEEE) received the B.S. degree in mechanical engineering from Shandong Agricultural University in 2012, and the Ph.D. degree in mechanical engineering from Xi’an Jiaotong University in 2019. He is currently an Associate Professor of mechanical engineering with Xi’an Jiaotong University. He was also a Visiting Scholar with Georgia Institute of Technology, USA. His research interests include machinery condition monitoring, intelligent fault diagnostics, and remaining useful life prediction of machinery

Asoke K. Nandi (Life Fellow, IEEE) received the Ph.D. degree in physics from the University of Cambridge (Trinity College), UK in 1978. He held academic positions in several universities, including the University of Oxford, UK; Imperial College London, UK; the University of Strathclyde, UK; and the University of Liverpool, UK; and a Finland Distinguished Professorship in Jyvaskyla, Finland. In 2013, he moved to Brunel University London, UK, to become the Chair and the Head of the Department of Electronic and Computer Engineering. He is also a Distinguished Visiting Professor with Xi’an Jiaotong University. His research interests include signal processing and machine learning, with applications to communications, image segmentations, and biomedical data. He has made many fundamental theoretical and algorithmic contributions to many aspects of signal processing and machine learning. He has much expertise in big and heterogeneous data, dealing with modeling, classification, estimation, and prediction. In 1983, he codiscovered the three fundamental particles known as W+, W, and Z0 (by the UA1 Team at CERN), providing the evidence for the unification of the electromagnetic and weak forces, for which the Nobel Committee for Physics in 1984 awarded the prize to his two team leaders for their decisive contributions. He has authored over 600 technical publications, including 250 journal articles as well as six books, titled Condition Monitoring with Vibration Signals: Image Segmentation: Principles, Techniques and Applications (Wiley, 2022), Compressive Sampling and Learning Algorithms for Rotating Machines (Wiley, 2020), Automatic Modulation Classification: Principles, Algorithms and Applications (Wiley, 2015), Integrative Cluster Analysis in Bioinformatics (Wiley, 2015), Blind Estimation Using Higher-Order Statistics (Springer, 1999), and Automatic Modulation Recognition of Communications Signals (Springer, 1996).Dr. Nandi is a Fellow of the Royal Academy of Engineering, UK, and a Member of Academia Europaea as well as a Fellow of seven other institutions, including the IET. Among the many awards, he received the Institute of Electrical and Electronics Engineers (USA) Heinrich Hertz Award in 2012, the Glory of Bengal Award for his outstanding achievements in scientific research in 2010, the Water Arbitration Prize of the Institution of Mechanical Engineers, UK in 1999, and the Mountbatten Premium, Division Award of the Electronics and Communications Division, Institution of Electrical Engineers, UK in 1998. The H-index of his publications is 85 (Google Scholar) and his ERDOS number is 2. He was an IEEE EMBS Distinguished Lecturer from 2018 to 2019
Corresponding author: Yaguo Lei, e-mail: yaguolei@mail.xjtu.edu.cn
Received Date: 2023-07-25
Revised Date: 2023-09-21
Accepted Date: 2023-10-22

Available Online: 2024-01-31

Abstract

Abstract

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain. However, in engineering scenarios, achieving such high-quality label annotation is difficult and expensive. The incorrect label annotation produces two negative effects: 1) the complex decision boundary of diagnosis models lowers the generalization performance on the target domain, and 2) the distribution of target domain samples becomes misaligned with the false-labeled samples. To overcome these negative effects, this article proposes a solution called the label recovery and trajectory designable network (LRTDN). LRTDN consists of three parts. First, a residual network with dual classifiers is to learn features from cross-domain samples. Second, an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain. With the training of relabeled samples, the complexity of diagnosis model is reduced via semi-supervised learning. Third, the adaptation trajectories are designed for sample distributions across domains. This ensures that the target domain samples are only adapted with the pure-labeled samples. The LRTDN is verified by two case studies, in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines. The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.
- Deep transfer learning,
- domain adaptation,
- incorrect label annotation,
- intelligent fault diagnosis,
- rotating machines

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References

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A semi-supervised transfer learning framework is presented for intelligent diagnosis
Domain adaptation is achieved in the presence of a false-labeled source domain
A new indicator is proposed to evaluate and further modify label anomaly
The adaptation trajectory to false-labeled samples is avoided by OT theory

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

doi: 10.1109/JAS.2023.124083

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