Learning to better see the unseen: Broad-Deep Mixed Anti-Forgetting
  Framework for Incremental Zero-Shot Fault Diagnosis
Jiancheng Zhao Jiaqi Yue Chunhui Zhao
Zero-shot fault diagnosis (ZSFD) is capable of identifying unseen faults via
predicting fault attributes labeled by human experts. We first recognize the
demand of ZSFD to deal with continuous changes in industrial processes, i.e.,
the model's ability to adapt to new fault categories and attributes while
avoiding forgetting the diagnosis ability learned previously. To overcome the
issue that the existing ZSFD paradigm cannot learn from evolving streams of
training data in industrial scenarios, the incremental ZSFD (IZSFD) paradigm is
proposed for the first time, which incorporates category increment and
attribute increment for both traditional ZSFD and generalized ZSFD paradigms.
To achieve IZSFD, we present a broad-deep mixed anti-forgetting framework
(BDMAFF) that aims to learn from new fault categories and attributes. To tackle
the issue of forgetting, BDMAFF effectively accumulates previously acquired
knowledge from two perspectives: features and attribute prototypes. The feature
memory is established through a deep generative model that employs
anti-forgetting training strategies, ensuring the generation quality of
historical categories is supervised and maintained. The diagnosis model SEEs
the UNSEEN faults with the help of generated samples from the generative model.
The attribute prototype memory is established through a diagnosis model
inspired by the broad learning system. Unlike traditional incremental learning
algorithms, BDMAFF introduces a memory-driven iterative update strategy for the
diagnosis model, which allows the model to learn new faults and attributes
without requiring the storage of all historical training samples. The
effectiveness of the proposed method is verified by a real hydraulic system and
the Tennessee-Eastman benchmark process.