Fuzzy-membership-based labeling: a new labeling method for both classification task and regression task



Published Sep 4, 2023
Diwang Ruan Zhaorong Li Yuheng Wu Jianping Yan Clemens Gühmann


In the machine learning and deep learning field, there are two main kinds of tasks: classification and regression. The label for the former is discrete, while for the latter is continuous. Due to the big gaps in labels, these two tasks are generally re solved separately, bringing low training efficiency and waste of computing resources. To this end, this paper proposes a new labeling method based on fuzzy membership. The main idea is to build an intermediate variable, which behaves between continuous and discrete variables. Then, the relation between the intermediate variable and the discrete label can be identified with fuzzy membership. Finally, the fuzzy membership is adopted for building labels to train the source model. After training, the source model can be transferred to achieve both classification and regression tasks. To validate the new labeling method, two typical tasks in the PHM field, aging stage classification and RUL prediction, are selected as the representative for classification and regression tasks, respectively. Furthermore, LSTM with two dense layers is chosen as the benchmark source model. With the C-MAPSS dataset, the superiority of the proposed fuzzy-membership based labeling to improve the network’s task transfer learning performance has been verified.

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fuzzy-membership-based labeling, classification, regression, task transfer learning, long short-term memory, remaining useful life (RUL) prediction

Alfeo, A. L., Cimino, M. G., & Vaglini, G. (2022). Degradation stage classification via interpretable feature learning. Journal of Manufacturing Systems, 62, 972–983.

Bote-Garcia, J.-L., Mokthari, N., & G¨uhmann, C. (2020). Wear monitoring of journal bearings with acoustic emission under different operating conditions. Spectrum, 1010, 49xx.

D. Frederick, J. D., & Litt, J. (2007). User’s guide for the commercial modular aero-propulsion system simulation (cmapss). In Technical manual tm2007-215026. NASA/ARL.

Djurdjanovic, D., Lee, J., & Ni, J. (2003). Watchdog agent—an infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics, 17(3-4), 109– 125.

Heimes, F. O. (2008). Recurrent neural networks for remaining useful life estimation. In 2008 international conference on prognostics and health management (pp. 1–6).

Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587.

Liu, R., Yang, B., & Hauptmann, A. G. (2019). Simultaneous bearing fault recognition and remaining useful life prediction using joint-loss convolutional neural network. IEEE Transactions on Industrial Informatics, 16(1), 87–96.

Mandal, S. N., Choudhury, J. P., & Chaudhuri, S. B. (2012). In search of suitable fuzzy membership function in prediction of time series data. International Journal of Computer Science Issues, 9(3), 293–302.

Mokhtari, N., Pelham, J. G., Nowoisky, S., Bote-Garcia, J. L., & Guhmann, C. (2020). Friction and wear monitoring methods for journal bearings of geared turbofans based on acoustic emission signals and machine learning. Lubricants, 8(3), 29.

Ramasso, E., & Saxena, A. (2014). Review and analysis of algorithmic approaches developed for prognostics on cmapss dataset. In Annual conference of the prognostics and health management society 2014.

Ruan, D., Chen, Y., Guhmann, C., Yan, J., & Li, Z. (2022). Dynamics modeling of bearing with defect in modelica and application in direct transfer learning from simulation to test bench for bearing fault diagnosis. Electronics, 11(4), 622.

Ruan, D., Wu, Y., & Yan, J. (2021). Remaining useful life prediction for aero-engine based on lstm and cnn. In 2021 33rd chinese control and decision conference (ccdc) (pp. 6706–6712).

Ruan, D., Wu, Y., Yan, J., & G¨uhmann, C. (2022). Fuzzymembership based framework for task transfer learning between fault diagnosis and rul prediction. IEEE Transactions on Reliability.

Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016). A survey of transfer learning. Journal of Big data, 3(1), 1–40.

Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by lotfi a zadeh (pp. 394–432). World Scientific.

Zhao, H., Liu, J., Chen, H., Chen, J., Li, Y., Xu, J., & Deng, W. (2022). Intelligent diagnosis using continuous wavelet transform and gauss convolutional deep belief network. IEEE Transactions on Reliability.

Zhou, J., Qin, Y., Chen, D., Liu, F., & Qian, Q. (2022). Remaining useful life prediction of bearings by a new reinforced memory gru network. Advanced Engineering Informatics, 53, 101682.
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