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Volume 6 Issue 5
Sep.  2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2019  >  6(5): 1268-1280
Xiaogang Wang, Xiyu Liu and Yu Li, "An Incremental Model Transfer Method for Complex Process Fault Diagnosis," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1268-1280, Sept. 2019. doi: 10.1109/JAS.2019.1911618
Citation: Xiaogang Wang, Xiyu Liu and Yu Li, "An Incremental Model Transfer Method for Complex Process Fault Diagnosis," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1268-1280, Sept. 2019. doi: 10.1109/JAS.2019.1911618
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An Incremental Model Transfer Method for Complex Process Fault Diagnosis

doi: 10.1109/JAS.2019.1911618

  • College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

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    Abstract
  • Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.

     

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    Highlights

    • Introducing model transfer into fault diagnosis field, IMTL achieves fault diagnosis with very few labeled samples. And fault detection and classification are achieved at the same time.
    • Combining model transfer with incremental learning, the proposed method (IMTL) achieves the extension from identifying the existing N faults in the old process to N+M faults in the new process, where M is the number of new faults in the new process.
    • Breaking the limit of [17] that the new process can only have one more class of faults than the old process, IMTL can identify M faults more in the new process.
    • IMTL gives a solution to a series of problems caused by the increase of fault classes.

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